https://github.com/iml-wg/HEPML-LivingReview

Living Review of Machine Learning for Particle Physics
https://github.com/iml-wg/HEPML-LivingReview

hep latex machine-learning papers particle-physics

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Living Review of Machine Learning for Particle Physics

• Host: GitHub
• URL: https://github.com/iml-wg/HEPML-LivingReview
• Owner: iml-wg
• Created: 2020-04-22T19:43:56.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-05-28T15:52:31.000Z (6 months ago)
• Last Synced: 2024-05-29T00:36:46.643Z (6 months ago)
• Topics: hep, latex, machine-learning, papers, particle-physics
• Language: TeX
• Homepage: https://iml-wg.github.io/HEPML-LivingReview
• Size: 2.63 MB
• Stars: 320
• Watchers: 27
• Forks: 98
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • Code of conduct: docs/code_of_conduct.md

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README

        
#  **A Living Review of Machine Learning for Particle Physics**

*Modern machine learning techniques, including deep learning, is rapidly being applied, adapted, and developed for high energy physics.  The goal of this document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses.  As a living document, it will be updated as often as possible to incorporate the latest developments.  A list of proper (unchanging) reviews can be found within.  Papers are grouped into a small set of topics to be as useful as possible.  Suggestions are most welcome.*

[![download](https://img.shields.io/badge/download-review-blue.svg)](https://iml-wg.github.io/HEPML-LivingReview/assets/hepml_review.pdf)

[![github](https://badges.aleen42.com/src/github.svg)](https://github.com/iml-wg/HEPML-LivingReview)

The purpose of this note is to collect references for modern machine learning as applied to particle physics. A minimal number of categories is chosen in order to be as useful as possible. Note that papers may be referenced in more than one category. The fact that a paper is listed in this document does not endorse or validate its content - that is for the community (and for peer-review) to decide. Furthermore, the classification here is a best attempt and may have flaws - please let us know if (a) we have missed a paper you think should be included, (b) a paper has been misclassified, or (c) a citation for a paper is not correct or if the journal information is now available. In order to be as useful as possible, this document will continue to evolve so please check back before you write your next paper. If you find this review helpful, please consider citing it using ```\cite{hepmllivingreview}``` in `HEPML.bib`.

This review was built with the help of the HEP-ML community, the [INSPIRE REST API](https://github.com/inspirehep/rest-api-doc), and the moderators Benjamin Nachman, Matthew Feickert, Claudius Krause, Johnny Raine, and Ramon Winterhalder.

##  Reviews

###  Modern reviews

* [Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning](https://arxiv.org/abs/1709.04464) [[DOI](https://doi.org/10.1016/j.physrep.2019.11.001)]

* [Deep Learning and its Application to LHC Physics](https://arxiv.org/abs/1806.11484) [[DOI](https://doi.org/10.1146/annurev-nucl-101917-021019)]

* [Machine Learning in High Energy Physics Community White Paper](https://arxiv.org/abs/1807.02876) [[DOI](https://doi.org/10.1088/1742-6596/1085/2/022008)]

* [Machine learning at the energy and intensity frontiers of particle physics](https://doi.org/10.1038/s41586-018-0361-2)

* [Machine learning and the physical sciences](https://arxiv.org/abs/1903.10563) [[DOI](https://doi.org/10.1103/RevModPhys.91.045002)]

* [Machine and Deep Learning Applications in Particle Physics](https://arxiv.org/abs/1912.08245) [[DOI](https://doi.org/10.1142/S0217751X19300199)]

* [Modern Machine Learning and Particle Physics](https://arxiv.org/abs/2103.12226) [[DOI](https://doi.org/10.1162/99608f92.beeb1183)]

* [Machine Learning in the Search for New Fundamental Physics](https://arxiv.org/abs/2112.03769)

* [Artificial Intelligence and Machine Learning in Nuclear Physics](https://arxiv.org/abs/2112.02309) [[DOI](https://doi.org/10.1103/RevModPhys.94.031003)]

* [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://arxiv.org/abs/2209.07559)

###  Specialized reviews

* [The Machine Learning Landscape of Top Taggers](https://arxiv.org/abs/1902.09914) [[DOI](https://doi.org/10.21468/SciPostPhys.7.1.014)]

* [Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review](https://arxiv.org/abs/2007.09121)

* [Graph neural networks in particle physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/{10.1088/2632-2153/abbf9a)]

* [A Review on Machine Learning for Neutrino Experiments](https://arxiv.org/abs/2008.01242) [[DOI](https://doi.org/10.1142/S0217751X20430058)]

* [Generative Networks for LHC events](https://arxiv.org/abs/2008.08558)

* [Parton distribution functions](https://arxiv.org/abs/2008.12305)

* [Simulation-based inference methods for particle physics](https://arxiv.org/abs/2010.06439)

* [Anomaly Detection for Physics Analysis and Less than Supervised Learning](https://arxiv.org/abs/2010.14554)

* [Graph Neural Networks for Particle Tracking and Reconstruction](https://arxiv.org/abs/2012.01249) [[DOI](https://doi.org/10.1142/9789811234033_0012)]

* [Distributed Training and Optimization Of Neural Networks](https://arxiv.org/abs/2012.01839) [[DOI](https://doi.org/10.1142/9789811234033_0008)]

* [The frontier of simulation-based inference](https://arxiv.org/abs/1911.01429) [[DOI](https://doi.org/10.1073/pnas.1912789117)]

* [Machine Learning scientific competitions and datasets](https://arxiv.org/abs/2012.08520)

* [Image-Based Jet Analysis](https://arxiv.org/abs/2012.09719)

* [Quantum Machine Learning in High Energy Physics](https://arxiv.org/abs/2005.08582) [[DOI](https://doi.org/10.1088/2632-2153/abc17d)]

* [Sequence-based Machine Learning Models in Jet Physics](https://arxiv.org/abs/2102.06128)

* [A survey of machine learning-based physics event generation](https://arxiv.org/abs/2106.00643) [[DOI](https://doi.org/10.24963/ijcai.2021/588)]

* [Deep Learning From Four Vectors](https://arxiv.org/abs/2203.03067)

* [Solving Simulation Systematics in and with AI/ML](https://arxiv.org/abs/2203.06112)

* [Symmetry Group Equivariant Architectures for Physics](https://arxiv.org/abs/2203.06153)

* [Machine Learning and LHC Event Generation](https://arxiv.org/abs/2203.07460) [[DOI](https://doi.org/10.21468/SciPostPhys.14.4.079)]

* [Machine Learning and Cosmology](https://arxiv.org/abs/2203.08056)

* [New directions for surrogate models and differentiable programming for High Energy Physics detector simulation](https://arxiv.org/abs/2203.08806)

* [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://arxiv.org/abs/2203.12852)

* [Physics Community Needs, Tools, and Resources for Machine Learning](https://arxiv.org/abs/2203.16255)

* [Boosted decision trees](https://arxiv.org/abs/2206.09645) [[url](https://doi.org/10.1142/9789811234033_0002)]

* [Data Science and Machine Learning in Education](https://arxiv.org/abs/2207.09060)

* [Interpretable Uncertainty Quantification in AI for HEP](https://arxiv.org/abs/2208.03284) [[DOI](https://doi.org/10.2172/1886020)]

* [Modern Machine Learning for LHC Physicists](https://arxiv.org/abs/2211.01421)

* [Bridging Machine Learning and Sciences: Opportunities and Challenges](https://arxiv.org/abs/2210.13441)

* [FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective](https://arxiv.org/abs/2210.08973) [[DOI](https://doi.org/10.1038/s41597-023-02298-6)]

* [Snowmass Neutrino Frontier Report](https://arxiv.org/abs/2211.08641)

* [Exploring QCD matter in extreme conditions with Machine Learning](https://arxiv.org/abs/2303.15136) [[DOI](https://doi.org/10.1016/j.ppnp.2023.104084)]

* [Graph neural networks at the Large Hadron Collider](https://doi.org/10.1038/s42254-023-00569-0)

* [Overview: Jet quenching with machine learning](https://arxiv.org/abs/2308.10035)

* [Artificial Intelligence for the Electron Ion Collider (AI4EIC)](https://arxiv.org/abs/2307.08593) [[DOI](https://doi.org/10.1007/s41781-024-00113-4)]

* [Deep Generative Models for Detector Signature Simulation: An Analytical Taxonomy](https://arxiv.org/abs/2312.09597)

* [Machine Learning for Anomaly Detection in Particle Physics](https://arxiv.org/abs/2312.14190) [[DOI](https://doi.org/10.1016/j.revip.2024.100091)]

* [Les Houches guide to reusable ML models in LHC analyses](https://arxiv.org/abs/2312.14575)

* [The SMARTHEP European Training Network](https://arxiv.org/abs/2401.13484) [[DOI](https://doi.org/10.1051/epjconf/202429508022)]

* [High-energy physics image classification: A Survey of Jet Applications](https://arxiv.org/abs/2403.11934)

* [Unsupervised and lightly supervised learning in particle physics](https://arxiv.org/abs/2403.13676)

* [Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC](https://arxiv.org/abs/2404.01071)

* [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807)

* [A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation](https://arxiv.org/abs/2406.12898)

* [Top-philic Machine Learning](https://arxiv.org/abs/2407.00183) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01237-9)]

* [QCD Masterclass Lectures on Jet Physics and Machine Learning](https://arxiv.org/abs/2407.04897)

* [TASI Lectures on Physics for Machine Learning](https://arxiv.org/abs/2408.00082)

###  Classical papers

* [Neural Networks and Cellular Automata in Experimental High-energy Physics](https://doi.org/10.1016/0010-4655(88)90004-5)

* [Finding Gluon Jets With a Neural Trigger](https://doi.org/10.1103/PhysRevLett.65.1321)

###  Datasets

* [The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics](https://arxiv.org/abs/2101.08320) [[DOI](https://doi.org/10.1088/1361-6633/ac36b9)]

* [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://arxiv.org/abs/2105.14027) [[DOI](https://doi.org/10.21468/SciPostPhys.12.1.043)]

* [Shared Data and Algorithms for Deep Learning in Fundamental Physics](https://arxiv.org/abs/2107.00656) [[DOI](https://doi.org/10.1007/s41781-022-00082-6)]

* [LHC physics dataset for unsupervised New Physics detection at 40 MHz](https://arxiv.org/abs/2107.02157) [[DOI](https://doi.org/10.1038/s41597-022-01187-8)]

* [A FAIR and AI-ready Higgs Boson Decay Dataset](https://arxiv.org/abs/2108.02214) [[DOI](https://doi.org/10.1038/s41597-021-01109-0)]

* [Particle Transformer for Jet Tagging](https://arxiv.org/abs/2202.03772)

* [Public Kaggle Competition ''IceCube -- Neutrinos in Deep Ice''](https://arxiv.org/abs/2307.15289)

* [Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype](https://arxiv.org/abs/2309.06582)

##  Classification

###  Parameterized classifiers

* [Parameterized neural networks for high-energy physics](https://arxiv.org/abs/1601.07913) [[DOI](https://doi.org/10.1140/epjc/s10052-016-4099-4)]

* [Approximating Likelihood Ratios with Calibrated Discriminative  Classifiers](https://arxiv.org/abs/1506.02169)

* [E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once](https://arxiv.org/abs/2101.07263) [[DOI](https://doi.org/10.1103/PhysRevD.103.116013)]

* [Boosting likelihood learning with event reweighting](https://arxiv.org/abs/2308.05704) [[DOI](https://doi.org/10.1007/JHEP03(2024)117)]

### Representations

####  Jet images

* [How to tell quark jets from gluon jets](https://doi.org/10.1103/PhysRevD.44.2025)

* [Jet-Images: Computer Vision Inspired Techniques for Jet Tagging](https://arxiv.org/abs/1407.5675) [[DOI](https://doi.org/10.1007/JHEP02(2015)118)]

* [Playing Tag with ANN: Boosted Top Identification with Pattern Recognition](https://arxiv.org/abs/1501.05968) [[DOI](https://doi.org/10.1007/JHEP07(2015)086)]

* [Jet-images — deep learning edition](https://arxiv.org/abs/1511.05190) [[DOI](https://doi.org/10.1007/JHEP07(2016)069)]

* [Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector](http://cds.cern.ch/record/2275641)

* [Boosting $H\to b\bar b$ with Machine Learning](https://arxiv.org/abs/1807.10768) [[DOI](https://doi.org/10.1007/JHEP10(2018)101)]

* [Learning to classify from impure samples with high-dimensional data](https://arxiv.org/abs/1801.10158) [[DOI](https://doi.org/10.1103/PhysRevD.98.011502)]

* [Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks](https://arxiv.org/abs/1609.00607) [[DOI](https://doi.org/10.1103/PhysRevD.95.014018)]

* [Deep learning in color: towards automated quark/gluon](https://arxiv.org/abs/1612.01551) [[DOI](https://doi.org/10.1007/JHEP01(2017)110)]

* [Deep-learning Top Taggers or The End of QCD?](https://arxiv.org/abs/1701.08784) [[DOI](https://doi.org/10.1007/JHEP05(2017)006)]

* [Pulling Out All the Tops with Computer Vision and Deep Learning](https://arxiv.org/abs/1803.00107) [[DOI](https://doi.org/10.1007/JHEP10(2018)121)]

* [Reconstructing boosted Higgs jets from event image segmentation](https://arxiv.org/abs/2008.13529) [[DOI](https://doi.org/10.1007/JHEP04(2021)156)]

* [An Attention Based Neural Network for Jet Tagging](https://arxiv.org/abs/2009.00170)

* [Quark-Gluon Jet Discrimination Using Convolutional Neural Networks](https://arxiv.org/abs/2012.02531) [[DOI](https://doi.org/10.3938/jkps.74.219)]

* [Learning to Isolate Muons](https://arxiv.org/abs/2102.02278) [[DOI](https://doi.org/10.1007/JHEP10(2021)200)]

* [Deep learning jet modifications in heavy-ion collisions](https://arxiv.org/abs/2012.07797) [[DOI](https://doi.org/10.1007/JHEP03(2021)206)]

* [Identifying the Quantum Properties of Hadronic Resonances using Machine Learning](https://arxiv.org/abs/2105.04582)

* [Automatic detection of boosted Higgs and top quark jets in event image](https://arxiv.org/abs/2302.13460) [[DOI](https://doi.org/10.1103/PhysRevD.108.116002)]

* [A Guide to Diagnosing Colored Resonances at Hadron Colliders](https://arxiv.org/abs/2306.00079) [[DOI](https://doi.org/10.1007/JHEP08(2023)173)]

* [High-energy physics image classification: A Survey of Jet Applications](https://arxiv.org/abs/2403.11934)

####  Event images

* [Topology classification with deep learning to improve real-time event selection at the LHC](https://arxiv.org/abs/1807.00083) [[DOI](https://doi.org/10.1007/s41781-019-0028-1)]

* [Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector](http://cds.cern.ch/record/2684070)

* [Boosting $H\to b\bar b$ with Machine Learning](https://arxiv.org/abs/1807.10768) [[DOI](https://doi.org/10.1007/JHEP10(2018)101)]

* [End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC](https://arxiv.org/abs/1807.11916) [[DOI](https://doi.org/10.1007/s41781-020-00038-8)]

* [Disentangling Boosted Higgs Boson Production Modes with Machine Learning](https://arxiv.org/abs/2009.05930) [[DOI](https://doi.org/10.1088/1748-0221/16/07/P07002)]

* [Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning](https://arxiv.org/abs/1910.11530) [[DOI](https://doi.org/10.1140/epjc/s10052-020-8030-7)]

* [End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data](https://arxiv.org/abs/2104.14659) [[DOI](https://doi.org/10.1051/epjconf/202125104030)]

* [Jet Single Shot Detection](https://arxiv.org/abs/2105.05785) [[DOI](https://doi.org/10.1051/epjconf/202125104027)]

* [Large-Scale Deep Learning for Multi-Jet Event Classification](https://arxiv.org/abs/2207.11710)

* [A Neural Network Approach for Orienting Heavy-Ion Collision Events](https://arxiv.org/abs/2308.15796) [[DOI](https://doi.org/10.1016/j.physletb.2023.138359)]

* [Exploring the Synergy of Kinematics and Dynamics for Collider Physics](https://arxiv.org/abs/2311.16674)

####  Sequences

* [Jet Flavor Classification in High-Energy Physics with Deep Neural Networks](https://arxiv.org/abs/1607.08633) [[DOI](https://doi.org/10.1103/PhysRevD.94.112002)]

* [Topology classification with deep learning to improve real-time event selection at the LHC](https://arxiv.org/abs/1807.00083) [[DOI](https://doi.org/10.1007/s41781-019-0028-1)]

* [Jet Flavour Classification Using DeepJet](https://arxiv.org/abs/2008.10519) [[DOI](https://doi.org/10.1088/1748-0221/15/12/P12012)]

* [Development of a Vertex Finding Algorithm using Recurrent Neural Network](https://arxiv.org/abs/2101.11906) [[DOI](https://doi.org/10.1016/j.nima.2022.167836)]

* [Sequence-based Machine Learning Models in Jet Physics](https://arxiv.org/abs/2102.06128)

* [Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment](http://cdsweb.cern.ch/record/2255226)

####  Trees

* [QCD-Aware Recursive Neural Networks for Jet Physics](https://arxiv.org/abs/1702.00748) [[DOI](https://doi.org/10.1007/JHEP01(2019)057)]

* [Recursive Neural Networks in Quark/Gluon Tagging](https://arxiv.org/abs/1711.02633) [[DOI](https://doi.org/10.1007/s41781-018-0007-y)]

* [Introduction and analysis of a method for the investigation of QCD-like tree data](https://arxiv.org/abs/2112.01809) [[DOI](https://doi.org/10.3390/e24010104)]

* [Applying Machine Learning Techniques to Searches for Lepton-Partner Pair-Production with Intermediate Mass Gaps at the Large Hadron Collider](https://arxiv.org/abs/2309.10197) [[DOI](https://doi.org/10.1103/PhysRevD.109.075018)]

* [Boosting dark matter searches at muon colliders with Machine Learning: the mono-Higgs channel as a case study](https://arxiv.org/abs/2309.11241) [[DOI](https://doi.org/10.1093/ptep/ptad144)]

* [Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection](https://arxiv.org/abs/2309.13111) [[DOI](https://doi.org/10.1103/PhysRevD.109.034033)]

* [Photon Classification with Gradient Boosted Trees at CLAS12](https://arxiv.org/abs/2402.13105)

* [Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks](https://arxiv.org/abs/2405.06040)

####  Graphs

* [Neural Message Passing for Jet Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_29.pdf)

* [Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors](https://arxiv.org/abs/2003.11603)

* [Probing stop pair production at the LHC with graph neural networks](https://arxiv.org/abs/1807.09088) [[DOI](https://doi.org/10.1007/JHEP08(2019)055)]

* [Pileup mitigation at the Large Hadron Collider with graph neural networks](https://arxiv.org/abs/1810.07988) [[DOI](https://doi.org/10.1140/epjp/i2019-12710-3)]

* [Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC](https://arxiv.org/abs/1901.05627) [[DOI](https://doi.org/10.1016/j.physletb.2020.135198)]

* [JEDI-net: a jet identification algorithm based on interaction networks](https://arxiv.org/abs/1908.05318) [[DOI](https://doi.org/10.1140/epjc/s10052-020-7608-4)]

* [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://arxiv.org/abs/1902.07987) [[DOI](https://doi.org/10.1140/epjc/s10052-019-7113-9)]

* [Interpretable deep learning for two-prong jet classification with jet spectra](https://arxiv.org/abs/1904.02092) [[DOI](https://doi.org/10.1007/JHEP07(2019)135)]

* [Towards a Computer Vision Particle Flow](https://arxiv.org/abs/2003.08863) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08897-0)]

* [Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions](https://arxiv.org/abs/2003.11787) [[DOI](https://doi.org/10.1007/JHEP07(2020)111)]

* [Probing triple Higgs coupling with machine learning at the LHC](https://arxiv.org/abs/2005.11086) [[DOI](https://doi.org/10.1103/PhysRevD.104.056003)]

* [Casting a graph net to catch dark showers](https://arxiv.org/abs/2006.08639) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.046)]

* [Graph neural networks in particle physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/{10.1088/2632-2153/abbf9a)]

* [Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics](https://arxiv.org/abs/2008.03601) [[DOI](https://doi.org/10.3389/fdata.2020.598927)]

* [Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons](https://arxiv.org/abs/2008.06064) [[DOI](https://doi.org/10.1103/PhysRevD.102.075014)]

* [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://arxiv.org/abs/2007.00149)

* [Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors](https://arxiv.org/abs/2009.00688) [[DOI](https://doi.org/10.1103/PhysRevD.103.032005)]

* [The Boosted Higgs Jet Reconstruction via Graph Neural Network](https://arxiv.org/abs/2010.05464) [[DOI](https://doi.org/10.1103/PhysRevD.103.116025)]

* [Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs](https://arxiv.org/abs/2012.01563)

* [Particle Track Reconstruction using Geometric Deep Learning](https://arxiv.org/abs/2012.08515)

* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)]

* [Vertex and Energy Reconstruction in JUNO with Machine Learning Methods](https://arxiv.org/abs/2101.04839) [[DOI](https://doi.org/10.1016/j.nima.2021.165527)]

* [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://arxiv.org/abs/2101.08578) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09158-w)]

* [Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC](https://arxiv.org/abs/2103.00916) [[DOI](https://doi.org/10.1051/epjconf/202125103047)]

* [Deep Learning strategies for ProtoDUNE raw data denoising](https://arxiv.org/abs/2103.01596) [[DOI](https://doi.org/10.1007/s41781-021-00077-9)]

* [Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2103.06233) [[DOI](https://doi.org/10.1051/epjconf/202125103054)]

* [Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC](https://arxiv.org/abs/2103.06509)

* [Charged particle tracking via edge-classifying interaction networks](https://arxiv.org/abs/2103.16701) [[DOI](https://doi.org/10.1007/s41781-021-00073-z)]

* [Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks](https://arxiv.org/abs/2103.14906)

* [Graph Generative Models for Fast Detector Simulations in High Energy Physics](https://arxiv.org/abs/2104.01725)

* [Segmentation of EM showers for neutrino experiments with deep graph neural networks](https://arxiv.org/abs/2104.02040) [[DOI](https://doi.org/10.1088/1748-0221/16/12/P12035)]

* [Anomaly detection with Convolutional Graph Neural Networks](https://arxiv.org/abs/2105.07988) [[DOI](https://doi.org/10.1007/JHEP08(2021)080)]

* [Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm](https://arxiv.org/abs/2109.14636) [[DOI](https://doi.org/10.1007/JHEP02(2022)060)]

* [Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks](https://arxiv.org/abs/2111.01838) [[DOI](https://doi.org/10.1007/JHEP04(2022)137)]

* [Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance](https://arxiv.org/abs/2111.12849)

* [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://arxiv.org/abs/2112.02048) [[DOI](https://doi.org/10.3389/fdata.2022.828666)]

* [Machine Learning for Particle Flow Reconstruction at CMS](https://arxiv.org/abs/2203.00330) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012100)]

* [An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging](https://arxiv.org/abs/2201.08187) [[DOI](https://doi.org/10.1007/JHEP07(2022)030)]

* [End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks](https://arxiv.org/abs/2204.01681) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10665-7)]

* [A jet tagging algorithm of graph network with HaarPooling message passing](https://arxiv.org/abs/2210.13869) [[DOI](https://doi.org/10.1103/PhysRevD.108.072007)]

* [PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics](https://arxiv.org/abs/2211.00454)

* [Climbing four tops with graph networks, transformers and pairwise features](https://arxiv.org/abs/2211.05143)

* [Reconstructing particles in jets using set transformer and hypergraph prediction networks](https://arxiv.org/abs/2212.01328) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11677-7)]

* [Do graph neural networks learn traditional jet substructure?](https://arxiv.org/abs/2211.09912)

* [Heterogeneous Graph Neural Network for identifying hadronically decayed tau leptons at the High Luminosity LHC](https://arxiv.org/abs/2301.00501) [[DOI](https://doi.org/10.1088/1748-0221/18/07/P07001)]

* [Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles](https://arxiv.org/abs/2301.05638) [[DOI](https://doi.org/10.1088/2632-2153/acd989)]

* [On the BSM reach of four top production at the LHC](https://arxiv.org/abs/2302.08281) [[DOI](https://doi.org/10.1103/PhysRevD.108.035001)]

* [Topological Reconstruction of Particle Physics Processes using Graph Neural Networks](https://arxiv.org/abs/2303.13937) [[DOI](https://doi.org/10.1103/PhysRevD.107.116019)]

* [Equivariant Graph Neural Networks for Charged Particle Tracking](https://arxiv.org/abs/2304.05293)

* [Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events](https://arxiv.org/abs/2307.06434)

* [Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics](https://arxiv.org/abs/2307.07289) [[DOI](https://doi.org/10.1007/s41781-024-00117-0)]

* [Determination of impact parameter for CEE with Digi-input neural networks](https://arxiv.org/abs/2307.15355) [[DOI](https://doi.org/10.1088/1748-0221/19/05/P05009)]

* [Domain-adversarial graph neural networks for \ensuremath{\Lambda} hyperon identification with CLAS12](https://arxiv.org/abs/2302.05481) [[DOI](https://doi.org/10.1088/1748-0221/18/06/P06002)]

* [Hierarchical Graph Neural Networks for Particle Track Reconstruction](https://arxiv.org/abs/2303.01640)

* [GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions](https://arxiv.org/abs/2304.08610) [[DOI](https://doi.org/10.1007/s41781-023-00107-8)]

* [Flavour tagging with graph neural networks with the ATLAS detector](https://arxiv.org/abs/2306.04415)

* [Photon Reconstruction in the Belle II Calorimeter Using Graph Neural Networks](https://arxiv.org/abs/2306.04179) [[DOI](https://doi.org/10.1007/s41781-023-00105-w)]

* [Jet energy calibration with deep learning as a Kubeflow pipeline](https://arxiv.org/abs/2308.12724) [[DOI](https://doi.org/10.1007/s41781-023-00103-y)]

* [LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC](https://arxiv.org/abs/2308.13611)

* [Graph Structure from Point Clouds: Geometric Attention is All You Need](https://arxiv.org/abs/2307.16662)

* [Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction](https://arxiv.org/abs/2309.17351) [[DOI](https://doi.org/10.1007/JHEP01(2024)113)]

* [Rotation-equivariant graph neural network for learning hadronic SMEFT effects](https://arxiv.org/abs/2401.10323) [[DOI](https://doi.org/10.1103/PhysRevD.109.076012)]

* [Combined track finding with GNN \& CKF](https://arxiv.org/abs/2401.16016)

* [Neutrino Reconstruction in TRIDENT Based on Graph Neural Network](https://arxiv.org/abs/2401.15324) [[DOI](https://doi.org/10.1007/978-981-97-0065-3_20)]

* [Application of Graph Neural Networks in Dark Photon Search with Visible Decays at Future Beam Dump Experiment](https://arxiv.org/abs/2401.15477) [[DOI](https://doi.org/10.1007/978-981-97-0065-3_19)]

* [Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning](https://arxiv.org/abs/2402.10149)

* [A new graph-neural-network flavor tagger for Belle II and measurement of $\sin 2\phi_1$ in $B^0 \to J/\psi K^0_\text{S}$ decays](https://arxiv.org/abs/2402.17260)

* [A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics](https://arxiv.org/abs/2402.17386)

* [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872)

* [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106)

* [Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter](https://arxiv.org/abs/2406.11937)

* [Accelerating Graph-based Tracking Tasks with Symbolic Regression](https://arxiv.org/abs/2406.16752)

* [Graph Neural Network-Based Track Finding in the LHCb Vertex Detector](https://arxiv.org/abs/2407.12119)

* [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925)

####  Sets (point clouds)

* [Energy Flow Networks: Deep Sets for Particle Jets](https://arxiv.org/abs/1810.05165) [[DOI](https://doi.org/10.1007/JHEP01(2019)121)]

* [ParticleNet: Jet Tagging via Particle Clouds](https://arxiv.org/abs/1902.08570) [[DOI](https://doi.org/10.1103/PhysRevD.101.056019)]

* [ABCNet: An attention-based method for particle tagging](https://arxiv.org/abs/2001.05311) [[DOI](https://doi.org/10.1140/epjp/s13360-020-00497-3)]

* [Secondary Vertex Finding in Jets with Neural Networks](https://arxiv.org/abs/2008.02831) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09342-y)]

* [Equivariant Energy Flow Networks for Jet Tagging](https://arxiv.org/abs/2012.00964) [[DOI](https://doi.org/10.1103/PhysRevD.103.074022)]

* [Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks](https://arxiv.org/abs/2010.09206) [[DOI](https://doi.org/10.1103/PhysRevD.105.112008)]

* [Zero-Permutation Jet-Parton Assignment using a Self-Attention Network](https://arxiv.org/abs/2012.03542) [[DOI](https://doi.org/10.1007/s40042-024-01037-3)]

* [Learning to Isolate Muons](https://arxiv.org/abs/2102.02278) [[DOI](https://doi.org/10.1007/JHEP10(2021)200)]

* [Point Cloud Transformers applied to Collider Physics](https://arxiv.org/abs/2102.05073) [[DOI](https://doi.org/10.1088/2632-2153/ac07f6)]

* [SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention](https://arxiv.org/abs/2106.03898) [[DOI](https://doi.org/10.21468/SciPostPhys.12.5.178)]

* [Particle Convolution for High Energy Physics](https://arxiv.org/abs/2107.02908)

* [Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS](https://cds.cern.ch/record/2718948)

* [Particle Transformer for Jet Tagging](https://arxiv.org/abs/2202.03772)

* [Point Cloud Generation using Transformer Encoders and Normalising Flows](https://arxiv.org/abs/2211.13623)

* [Comparing Point Cloud Strategies for Collider Event Classification](https://arxiv.org/abs/2212.10659) [[DOI](https://doi.org/10.1103/PhysRevD.108.012001)]

* [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979)

* [Attention to Mean-Fields for Particle Cloud Generation](https://arxiv.org/abs/2305.15254)

* [A data-driven and model-agnostic approach to solving combinatorial assignment problems in searches for new physics](https://arxiv.org/abs/2309.05728) [[DOI](https://doi.org/10.1103/PhysRevD.109.L011702)]

* [EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion](https://arxiv.org/abs/2310.00049)

* [The Optimal use of Segmentation for Sampling Calorimeters](https://arxiv.org/abs/2310.04442)

* [PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts](https://arxiv.org/abs/2311.11011)

* [Multi-scale cross-attention transformer encoder for event classification](https://arxiv.org/abs/2401.00452) [[DOI](https://doi.org/10.1007/JHEP03(2024)144)]

* [Sets are All You Need: Ultrafast Jet Classification on FPGAs for HL-LHC](https://arxiv.org/abs/2402.01876)

* [Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling](https://arxiv.org/abs/2403.08854)

####  Physics-inspired basis

* [Automating the Construction of Jet Observables with Machine Learning](https://arxiv.org/abs/1902.07180) [[DOI](https://doi.org/10.1103/PhysRevD.100.095016)]

* [How Much Information is in a Jet?](https://arxiv.org/abs/1704.08249) [[DOI](https://doi.org/10.1007/JHEP06(2017)073)]

* [Novel Jet Observables from Machine Learning](https://arxiv.org/abs/1710.01305) [[DOI](https://doi.org/10.1007/JHEP03(2018)086)]

* [Energy flow polynomials: A complete linear basis for jet substructure](https://arxiv.org/abs/1712.07124) [[DOI](https://doi.org/10.1007/JHEP04(2018)013)]

* [Deep-learned Top Tagging with a Lorentz Layer](https://arxiv.org/abs/1707.08966) [[DOI](https://doi.org/10.21468/SciPostPhys.5.3.028)]

* [Resurrecting $b\bar{b}h$ with kinematic shapes](https://arxiv.org/abs/2011.13945) [[DOI](https://doi.org/10.1007/JHEP04(2021)139)]

* [Decay-aware neural network for event classification in collider physics](https://arxiv.org/abs/2212.08759)

* [Jet SIFT-ing: a new scale-invariant jet clustering algorithm for the substructure era](https://arxiv.org/abs/2302.08609) [[DOI](https://doi.org/10.1103/PhysRevD.108.016005)]

* [Retrieval of Boost Invariant Symbolic Observables via Feature Importance](https://arxiv.org/abs/2306.13496)

* [Learning Broken Symmetries with Resimulation and Encouraged Invariance](https://arxiv.org/abs/2311.05952)

* [Jet Rotational Metrics](https://arxiv.org/abs/2311.06686)

* [JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables](https://arxiv.org/abs/2311.14654)

* [Exploring the Truth and Beauty of Theory Landscapes with Machine Learning](https://arxiv.org/abs/2401.11513)

* [Exotic and physics-informed support vector machines for high energy physics](https://arxiv.org/abs/2407.03538)

* [Physics-informed machine learning approaches to reactor antineutrino detection](https://arxiv.org/abs/2407.06139)

* [Universal New Physics Latent Space](https://arxiv.org/abs/2407.20315)

### Targets

####  $W/Z$ tagging

* [Jet-images — deep learning edition](https://arxiv.org/abs/1511.05190) [[DOI](https://doi.org/10.1007/JHEP07(2016)069)]

* [Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks](https://arxiv.org/abs/1609.00607) [[DOI](https://doi.org/10.1103/PhysRevD.95.014018)]

* [QCD-Aware Recursive Neural Networks for Jet Physics](https://arxiv.org/abs/1702.00748) [[DOI](https://doi.org/10.1007/JHEP01(2019)057)]

* [Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques](https://arxiv.org/abs/2004.08262) [[DOI](https://doi.org/10.1088/1748-0221/15/06/P06005)]

* [Boosted $W$ and $Z$ tagging with jet charge and deep learning](https://arxiv.org/abs/1908.08256) [[DOI](https://doi.org/10.1103/PhysRevD.101.053001)]

* [Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons](https://arxiv.org/abs/2008.06064) [[DOI](https://doi.org/10.1103/PhysRevD.102.075014)]

* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)]

* [A $W^\pm$ polarization analyzer from Deep Neural Networks](https://arxiv.org/abs/2102.05124)

* [Role of polarizations and spin-spin correlations of W's in e-e+\textrightarrow{}W-W+ at s](https://arxiv.org/abs/2212.12973) [[DOI](https://doi.org/10.1103/PhysRevD.107.073004)]

* [Gradient Boosting MUST taggers for highly-boosted jets](https://arxiv.org/abs/2305.04957)

* [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979)

* [Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks](https://arxiv.org/abs/2306.07726) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11931-y)]

* [Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes](https://arxiv.org/abs/2310.13009)

* [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)]

* [Interplay of Traditional Methods and Machine Learning Algorithms for Tagging Boosted Objects](https://arxiv.org/abs/2408.01138) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01256-6)]

####  $H\rightarrow b\bar{b}$

* [Automating the Construction of Jet Observables with Machine Learning](https://arxiv.org/abs/1902.07180) [[DOI](https://doi.org/10.1103/PhysRevD.100.095016)]

* [Boosting $H\to b\bar b$ with Machine Learning](https://arxiv.org/abs/1807.10768) [[DOI](https://doi.org/10.1007/JHEP10(2018)101)]

* [Interaction networks for the identification of boosted $H \rightarrow b\overline{b}$ decays](https://arxiv.org/abs/1909.12285) [[DOI](https://doi.org/10.1103/PhysRevD.102.012010)]

* [Interpretable deep learning for two-prong jet classification with jet spectra](https://arxiv.org/abs/1904.02092) [[DOI](https://doi.org/10.1007/JHEP07(2019)135)]

* [Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques](https://arxiv.org/abs/2004.08262) [[DOI](https://doi.org/10.1088/1748-0221/15/06/P06005)]

* [Disentangling Boosted Higgs Boson Production Modes with Machine Learning](https://arxiv.org/abs/2009.05930) [[DOI](https://doi.org/10.1088/1748-0221/16/07/P07002)]

* [Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC](https://arxiv.org/abs/2009.06754)

* [The Boosted Higgs Jet Reconstruction via Graph Neural Network](https://arxiv.org/abs/2010.05464) [[DOI](https://doi.org/10.1103/PhysRevD.103.116025)]

* [Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks](https://arxiv.org/abs/2010.08201)

* [Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process](https://arxiv.org/abs/2103.09129) [[DOI](https://doi.org/10.1140/epjp/s13360-022-03024-8)]

* [Higgs tagging with the Lund jet plane](https://arxiv.org/abs/2105.03989) [[DOI](https://doi.org/10.1103/PhysRevD.104.055043)]

####  quarks and gluons

* [Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector](http://cds.cern.ch/record/2275641)

* [Deep learning in color: towards automated quark/gluon](https://arxiv.org/abs/1612.01551) [[DOI](https://doi.org/10.1007/JHEP01(2017)110)]

* [Recursive Neural Networks in Quark/Gluon Tagging](https://arxiv.org/abs/1711.02633) [[DOI](https://doi.org/10.1007/s41781-018-0007-y)]

* [DeepJet: Generic physics object based jet multiclass classification for LHC experiments](https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf)

* [Probing heavy ion collisions using quark and gluon jet substructure](https://arxiv.org/abs/1803.03589)

* [JEDI-net: a jet identification algorithm based on interaction networks](https://arxiv.org/abs/1908.05318) [[DOI](https://doi.org/10.1140/epjc/s10052-020-7608-4)]

* [Quark-Gluon Tagging: Machine Learning vs Detector](https://arxiv.org/abs/1812.09223) [[DOI](https://doi.org/10.21468/SciPostPhys.6.6.069)]

* [Towards Machine Learning Analytics for Jet Substructure](https://arxiv.org/abs/2007.04319) [[DOI](https://doi.org/10.1007/JHEP09(2020)195)]

* [Quark Gluon Jet Discrimination with Weakly Supervised Learning](https://arxiv.org/abs/2012.02540) [[DOI](https://doi.org/10.3938/jkps.75.652)]

* [Quark-Gluon Jet Discrimination Using Convolutional Neural Networks](https://arxiv.org/abs/2012.02531) [[DOI](https://doi.org/10.3938/jkps.74.219)]

* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)]

* [Safety of Quark/Gluon Jet Classification](https://arxiv.org/abs/2103.09103)

* [Identifying the Quantum Properties of Hadronic Resonances using Machine Learning](https://arxiv.org/abs/2105.04582)

* [Quarks and gluons in the Lund plane](https://arxiv.org/abs/2112.09140) [[DOI](https://doi.org/10.1007/JHEP08(2022)177)]

* [Systematic Quark/Gluon Identification with Ratios of Likelihoods](https://arxiv.org/abs/2207.12411) [[DOI](https://doi.org/10.1007/JHEP12(2022)021)]

* [Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis](https://arxiv.org/abs/2304.07196) [[DOI](https://doi.org/10.21468/SciPostPhys.16.1.015)]

* [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979)

* [Quark/Gluon Discrimination and Top Tagging with Dual Attention Transformer](https://arxiv.org/abs/2307.04723) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12293-1)]

* [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300) [[DOI](https://doi.org/10.1007/JHEP09(2023)135)]

* [Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow](https://arxiv.org/abs/2312.03434)

* [Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer](https://arxiv.org/abs/2406.08590)

* [A multicategory jet image classification framework using deep neural network](https://arxiv.org/abs/2407.03524)

* [Jet Tagging with More-Interaction Particle Transformer](https://arxiv.org/abs/2407.08682)

####  top quark tagging

* [Playing Tag with ANN: Boosted Top Identification with Pattern Recognition](https://arxiv.org/abs/1501.05968) [[DOI](https://doi.org/10.1007/JHEP07(2015)086)]

* [DeepJet: Generic physics object based jet multiclass classification for LHC experiments](https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf)

* [The Machine Learning Landscape of Top Taggers](https://arxiv.org/abs/1902.09914) [[DOI](https://doi.org/10.21468/SciPostPhys.7.1.014)]

* [Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions](https://arxiv.org/abs/2003.11787) [[DOI](https://doi.org/10.1007/JHEP07(2020)111)]

* [CapsNets Continuing the Convolutional Quest](https://arxiv.org/abs/1906.11265) [[DOI](https://doi.org/10.21468/SciPostPhys.8.2.023)]

* [Deep-learned Top Tagging with a Lorentz Layer](https://arxiv.org/abs/1707.08966) [[DOI](https://doi.org/10.21468/SciPostPhys.5.3.028)]

* [Deep-learning Top Taggers or The End of QCD?](https://arxiv.org/abs/1701.08784) [[DOI](https://doi.org/10.1007/JHEP05(2017)006)]

* [Pulling Out All the Tops with Computer Vision and Deep Learning](https://arxiv.org/abs/1803.00107) [[DOI](https://doi.org/10.1007/JHEP10(2018)121)]

* [Boosted Top Quark Tagging and Polarization Measurement using Machine Learning](https://arxiv.org/abs/2010.11778) [[DOI](https://doi.org/10.1103/PhysRevD.105.042005)]

* [Morphology for Jet Classification](https://arxiv.org/abs/2010.13469) [[DOI](https://doi.org/10.1103/PhysRevD.105.014004)]

* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)]

* [Pulling the Higgs and Top needles from the jet stack with Feature Extended Supervised Tagging](https://arxiv.org/abs/2102.01667) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09530-w)]

* [End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data](https://arxiv.org/abs/2104.14659) [[DOI](https://doi.org/10.1051/epjconf/202125104030)]

* [Leveraging universality of jet taggers through transfer learning](https://arxiv.org/abs/2203.06210) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10469-9)]

* [Application of deep learning in top pair and single top quark production at the LHC](https://arxiv.org/abs/2203.12871) [[DOI](https://doi.org/10.1140/epjp/s13360-023-04409-z)]

* [BIP: Boost Invariant Polynomials for Efficient Jet Tagging](https://arxiv.org/abs/2207.08272) [[DOI](https://doi.org/10.1088/2632-2153/aca9ca)]

* [Boosted top tagging and its interpretation using Shapley values](https://arxiv.org/abs/2212.11606)

* [Automatic detection of boosted Higgs and top quark jets in event image](https://arxiv.org/abs/2302.13460) [[DOI](https://doi.org/10.1103/PhysRevD.108.116002)]

* [Machine Learning in Top Physics in the ATLAS and CMS Collaborations](https://arxiv.org/abs/2301.09534)

* [Quark/Gluon Discrimination and Top Tagging with Dual Attention Transformer](https://arxiv.org/abs/2307.04723) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12293-1)]

* [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)]

* [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300) [[DOI](https://doi.org/10.1007/JHEP09(2023)135)]

* [Investigating the Violation of Charge-parity Symmetry Through Top-quark ChromoElectric Dipole Moments by Using Machine Learning Techniques](https://arxiv.org/abs/2306.11683) [[DOI](https://doi.org/10.5506/APhysPolB.54.5-A4)]

* [ML-Based Top Taggers: Performance, Uncertainty and Impact of Tower \& Tracker Data Integration](https://arxiv.org/abs/2309.01568)

* [Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes](https://arxiv.org/abs/2310.13009)

* [19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics](https://arxiv.org/abs/2310.16121)

* [Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation](https://arxiv.org/abs/2311.14160)

* [Scaling Laws in Jet Classification](https://arxiv.org/abs/2312.02264)

* [Jet Classification Using High-Level Features from Anatomy of Top Jets](https://arxiv.org/abs/2312.11760)

* [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330) [[DOI](https://doi.org/10.1007/JHEP05(2024)004)]

* [The Phase Space Distance Between Collider Events](https://arxiv.org/abs/2405.16698)

* [Hadronic Top Quark Polarimetry with ParticleNet](https://arxiv.org/abs/2407.01663)

####  strange jets

* [Strange Jet Tagging](https://arxiv.org/abs/2003.09517)

* [A tagger for strange jets based on tracking information using long short-term memory](https://arxiv.org/abs/1907.07505) [[DOI](https://doi.org/10.1088/1748-0221/15/01/P01021)]

* [Maximum performance of strange-jet tagging at hadron colliders](https://arxiv.org/abs/2011.10736) [[DOI](https://doi.org/10.1088/1748-0221/16/08/P08039)]

* [Study of anomalous $W^-W^+\gamma/Z$ couplings using polarizations and spin correlations in $e^-e^+\to W^-W^+$ with polarized beams](https://arxiv.org/abs/2305.15106) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12292-2)]

####  $b$-tagging

* [Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV](https://arxiv.org/abs/1712.07158) [[DOI](https://doi.org/10.1088/1748-0221/13/05/P05011)]

* [Jet Flavor Classification in High-Energy Physics with Deep Neural Networks](https://arxiv.org/abs/1607.08633) [[DOI](https://doi.org/10.1103/PhysRevD.94.112002)]

* [The Full Event Interpretation}: {An Exclusive Tagging Algorithm for the Belle II Experiment](https://arxiv.org/abs/1807.08680) [[DOI](https://doi.org/10.1007/s41781-019-0021-8)]

* [Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors](https://arxiv.org/abs/2005.01842) [[DOI](https://doi.org/10.1088/1748-0221/16/03/P03017)]

* [Jet Flavour Classification Using DeepJet](https://arxiv.org/abs/2008.10519) [[DOI](https://doi.org/10.1088/1748-0221/15/12/P12012)]

* [Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment](http://cdsweb.cern.ch/record/2255226)

* [Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS](https://cds.cern.ch/record/2718948)

* [Performance studies of jet flavor tagging and measurement of $R_b$ using ParticleNet at CEPC](https://arxiv.org/abs/2208.13503) [[DOI](https://doi.org/10.1142/S0217751X23501683)]

* [Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface](https://arxiv.org/abs/2303.14511)

* [Fast $b$-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3](https://arxiv.org/abs/2306.09738) [[DOI](https://doi.org/10.1088/1748-0221/18/11/P11006)]

* [Neural networks for boosted di-$\tau$ identification](https://arxiv.org/abs/2312.08276)

* [Vertex Reconstruction with MaskFormers](https://arxiv.org/abs/2312.12272)

* [DNN-based identification of additional b jets for a differential $t\bar{t}b\bar{b}$ cross section measurement](https://arxiv.org/abs/2401.07626)

####  Flavor physics

* ['Deep' Dive into $b \to c$ Anomalies: Standardized and Future-proof Model Selection Using Self-normalizing Neural Networks](https://arxiv.org/abs/2008.04316)

* [Predicting Exotic Hadron Masses with Data Augmentation Using Multilayer Perceptron](https://arxiv.org/abs/2208.09538) [[DOI](https://doi.org/10.1142/S0217751X23500033)]

* [Revealing the nature of hidden charm pentaquarks with machine learning](https://arxiv.org/abs/2301.05364) [[DOI](https://doi.org/10.1016/j.scib.2023.04.018)]

* [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176) [[DOI](https://doi.org/10.1007/JHEP12(2023)021)]

* [Differentiable Vertex Fitting for Jet Flavour Tagging](https://arxiv.org/abs/2310.12804)

* [Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN](https://arxiv.org/abs/2402.16493)

* [Heavy quarkonium spectral function in an anisotropic background](https://arxiv.org/abs/2403.04966) [[DOI](https://doi.org/10.1103/PhysRevD.109.086010)]

* [A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements](https://arxiv.org/abs/2403.18265)

* [Meson mass and width: Deep learning approach](https://arxiv.org/abs/2404.00448)

* [Exploring Transport Properties of Quark-Gluon Plasma with a Machine-Learning assisted Holographic Approach](https://arxiv.org/abs/2404.18217)

* [Holographic complex potential of a quarkonium from deep learning](https://arxiv.org/abs/2406.06285)

####  BSM particles and models

* [Automating the Construction of Jet Observables with Machine Learning](https://arxiv.org/abs/1902.07180) [[DOI](https://doi.org/10.1103/PhysRevD.100.095016)]

* [Searching for Exotic Particles in High-Energy Physics with Deep Learning](https://arxiv.org/abs/1402.4735) [[DOI](https://doi.org/10.1038/ncomms5308)]

* [Interpretable deep learning for two-prong jet classification with jet spectra](https://arxiv.org/abs/1904.02092) [[DOI](https://doi.org/10.1007/JHEP07(2019)135)]

* [A deep neural network to search for new long-lived particles decaying to jets](https://arxiv.org/abs/1912.12238) [[DOI](https://doi.org/10.1088/2632-2153/ab9023)]

* [Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter](https://arxiv.org/abs/2004.10744) [[DOI](https://doi.org/10.1088/1748-0221/15/12/P12006)]

* [Casting a graph net to catch dark showers](https://arxiv.org/abs/2006.08639) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.046)]

* [Distinguishing $W'$ Signals at Hadron Colliders Using Neural Networks](https://arxiv.org/abs/2007.14586) [[DOI](https://doi.org/10.1103/PhysRevD.103.036016)]

* [Deep learnig analysis of the inverse seesaw in a 3-3-1 model at the LHC](https://arxiv.org/abs/2008.03409) [[DOI](https://doi.org/10.1016/j.physletb.2020.135931)]

* [Comparing Traditional and Deep-Learning Techniques of Kinematic Reconstruction for polarisation Discrimination in Vector Boson Scattering](https://arxiv.org/abs/2008.05316) [[DOI](https://doi.org/10.1140/epjc/s10052-020-08713-1)]

* [Invisible Higgs search through Vector Boson Fusion: A deep learning approach](https://arxiv.org/abs/2008.05434) [[DOI](https://doi.org/10.1140/epjc/s10052-020-08629-w)]

* [Sensing Higgs cascade decays through memory](https://arxiv.org/abs/2008.08611) [[DOI](https://doi.org/10.1103/PhysRevD.102.095027)]

* [Phenomenology of vector-like leptons with Deep Learning at the Large Hadron Collider](https://arxiv.org/abs/2010.01307) [[DOI](https://doi.org/10.1007/JHEP01(2021)076)]

* [WIMPs or else? Using Machine Learning to disentangle LHC signatures](https://arxiv.org/abs/1910.06058) [[DOI](https://doi.org/10.21468/SciPostPhys.10.6.151)]

* [Exploring the standard model EFT in VH production with machine learning](https://arxiv.org/abs/1902.05803) [[DOI](https://doi.org/10.1103/PhysRevD.100.035040)]

* [Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider](https://arxiv.org/abs/2105.07530) [[DOI](https://doi.org/10.1016/j.revip.2021.100063)]

* [Towards a method to anticipate dark matter signals with deep learning at the LHC](https://arxiv.org/abs/2105.12018) [[DOI](https://doi.org/10.21468/SciPostPhys.12.2.063)]

* [Top squark signal significance enhancement by different Machine Learning Algorithms](https://arxiv.org/abs/2106.06813) [[DOI](https://doi.org/10.1142/S0217751X22501974)]

* [Detecting an axion-like particle with machine learning at the LHC](https://arxiv.org/abs/2106.07018) [[DOI](https://doi.org/10.1007/JHEP11(2021)138)]

* [Unsupervised Hadronic SUEP at the LHC](https://arxiv.org/abs/2107.12379) [[DOI](https://doi.org/10.1007/JHEP12(2021)129)]

* [Extract the energy scale of anomalous $\gamma\gamma \to W^+W^-$ scattering in the vector boson scattering process using artificial neural networks](https://arxiv.org/abs/2107.13624) [[DOI](https://doi.org/10.1007/JHEP09(2021)085)]

* [Beyond Cuts in Small Signal Scenarios - Enhanced Sneutrino Detectability Using Machine Learning](https://arxiv.org/abs/2108.03125) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11532-9)]

* [Deep Learning Searches for Vector-Like Leptons at the LHC and Electron/Muon Colliders](https://arxiv.org/abs/2108.03926) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11314-3)]

* [Probing Higgs exotic decay at the LHC with machine learning](https://arxiv.org/abs/2109.03294) [[DOI](https://doi.org/10.1103/PhysRevD.105.035008)]

* [Machine Learning Optimized Search for the $Z'$ from $U(1)_{L_\mu-L_\tau}$ at the LHC](https://arxiv.org/abs/2109.07674)

* [Boosted decision trees in the era of new physics: a smuon analysis case study](https://arxiv.org/abs/2109.11815) [[DOI](https://doi.org/10.1007/JHEP04(2022)015)]

* [How to use Machine Learning to improve the discrimination between signal and background at particle colliders](https://arxiv.org/abs/2110.15099) [[DOI](https://doi.org/10.3390/app112211076)]

* [Event-level variables for semivisible jets using anomalous jet tagging](https://arxiv.org/abs/2111.12156)

* [Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning](https://arxiv.org/abs/2112.15312) [[DOI](https://doi.org/10.1007/JHEP09(2022)141)]

* [Influence of QCD parton shower in deep learning invisible Higgs through vector boson fusion](https://arxiv.org/abs/2201.01040) [[DOI](https://doi.org/10.1103/PhysRevD.105.113003)]

* [Solving Combinatorial Problems at Particle Colliders Using Machine Learning](https://arxiv.org/abs/2201.02205) [[DOI](https://doi.org/10.1103/PhysRevD.106.016001)]

* [Phenomenology at the Large Hadron Collider with Deep Learning: the case of vector-like quarks decaying to light jets](https://arxiv.org/abs/2204.12542) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10799-8)]

* [Active learning BSM parameter spaces](https://arxiv.org/abs/2204.13950) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11368-3)]

* [Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC](https://arxiv.org/abs/2203.14569) [[DOI](https://doi.org/10.1103/PhysRevD.106.055008)]

* [Probing highly collimated photon-jets with deep learning](https://arxiv.org/abs/2203.16703) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012114)]

* [Measuring the anomalous quartic gauge couplings in the $W^+W^-\to W^+W^-$ process at muon collider using artificial neural networks](https://arxiv.org/abs/2204.10034) [[DOI](https://doi.org/10.1007/JHEP09(2022)074)]

* [Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes](https://arxiv.org/abs/2207.04157) [[DOI](https://doi.org/10.1007/JHEP11(2022)045)]

* [Probing a $\mathrm{Z}^{\prime}$ with non-universal fermion couplings through top quark fusion, decays to bottom quarks, and machine learning techniques](https://arxiv.org/abs/2210.15813) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11506-x)]

* [VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger](https://arxiv.org/abs/2209.05518) [[DOI](https://doi.org/10.1103/PhysRevD.107.016014)]

* [Machine-enhanced CP-asymmetries in the electroweak sector](https://arxiv.org/abs/2209.05143) [[DOI](https://doi.org/10.1103/PhysRevD.107.016008)]

* [Learning to Identify Semi-Visible Jets](https://arxiv.org/abs/2208.10062) [[DOI](https://doi.org/10.1007/JHEP12(2022)132)]

* [Associated production of Higgs and single top at the LHC in presence of the SMEFT operators](https://arxiv.org/abs/2211.05450) [[DOI](https://doi.org/10.1007/JHEP08(2023)015)]

* [Machine learning-enhanced search for a vectorlike-singlet $\mathbf B$ quark decaying to a singlet scalar or pseudoscalar](https://arxiv.org/abs/2212.02442) [[DOI](https://doi.org/10.1103/PhysRevD.107.115001)]

* [Searching for exotic Higgs bosons from top quark decays at the HL-LHC](https://arxiv.org/abs/2212.09061)

* [Search for supersymmetry in final states with missing transverse momentum and three or more b-jets in 139 fb$^{-1}$ of proton\textendash{}proton collisions at $\sqrt{s}](https://arxiv.org/abs/2211.08028) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11543-6)]

* [Search for supersymmetry in final states with a single electron or muon using angular correlations and heavy-object identification in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2211.08476) [[DOI](https://doi.org/10.1007/JHEP09(2023)149)]

* [Search for Electroweak Production of Supersymmetric Particles in Compressed Mass Spectra With the ATLAS Detector at the LHC](https://arxiv.org/abs/2211.11642)

* [Search for a new scalar resonance in flavour-changing neutral-current top-quark decays $t \rightarrow qX$ ($q](https://arxiv.org/abs/2301.03902) [[DOI](https://doi.org/10.1007/JHEP07(2023)199)]

* [Probing Electroweak Phase Transition in Singlet scalar extension of Standard Model at HL-LHC through $bbZZ$ channel using parameterized machine learning](https://arxiv.org/abs/2302.04191)

* [Probing Heavy Neutrinos at the LHC from Fat-jet using Machine Learning](https://arxiv.org/abs/2303.15920)

* [Optimal Mass Variables for Semivisible Jets](https://arxiv.org/abs/2303.16253) [[DOI](https://doi.org/10.21468/SciPostPhysCore.6.4.067)]

* [Invariant mass reconstruction of heavy gauge bosons decaying to $\tau$ leptons using machine learning techniques](https://arxiv.org/abs/2304.01126) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12527-w)]

* [Searching for anomalous quartic gauge couplings at muon colliders using principle component analysis](https://arxiv.org/abs/2304.01505) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11719-0)]

* [Search for vector-like leptons at a Muon Collider](https://arxiv.org/abs/2304.01885) [[DOI](https://doi.org/10.1088/1674-1137/ace5a7)]

* [Probing Dark QCD Sector through the Higgs Portal with Machine Learning at the LHC](https://arxiv.org/abs/2304.03237) [[DOI](https://doi.org/10.1007/JHEP08(2023)187)]

* [Uncovering doubly charged scalars with dominant three-body decays using machine learning](https://arxiv.org/abs/2304.09195) [[DOI](https://doi.org/10.1007/JHEP11(2023)009)]

* [Searching for dark jets with displaced vertices using weakly supervised machine learning](https://arxiv.org/abs/2305.04372) [[DOI](https://doi.org/10.1103/PhysRevD.108.035036)]

* [Gradient Boosting MUST taggers for highly-boosted jets](https://arxiv.org/abs/2305.04957)

* [Leveraging on-shell interference to search for FCNCs of the top quark and the Z boson](https://arxiv.org/abs/2305.12172) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11982-1)]

* [Sharpening the $A\to Z^{(*)}h $ Signature of the Type-II 2HDM at the LHC through Advanced Machine Learning](https://arxiv.org/abs/2305.13781) [[DOI](https://doi.org/10.1007/JHEP11(2023)020)]

* [Improving sensitivity of trilinear RPV SUSY searches using machine learning at the LHC](https://arxiv.org/abs/2308.02697) [[DOI](https://doi.org/10.1103/PhysRevD.109.035001)]

* [LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC](https://arxiv.org/abs/2308.13611)

* [Machine Learning Classification of Sphalerons and Black Holes at the LHC](https://arxiv.org/abs/2310.15227) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12790-x)]

* [Probing Light Fermiophobic Higgs Boson via diphoton jets at the HL-LHC](https://arxiv.org/abs/2310.17741) [[DOI](https://doi.org/10.1103/PhysRevD.109.015017)]

* [Optimize the event selection strategy the study the anomalous quartic gauge couplings at muon colliders using the support vector machine](https://arxiv.org/abs/2311.15280)

* [Quantum Metric Learning for New Physics Searches at the LHC](https://arxiv.org/abs/2311.16866)

* [Multi-scale cross-attention transformer encoder for event classification](https://arxiv.org/abs/2401.00452) [[DOI](https://doi.org/10.1007/JHEP03(2024)144)]

* [Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques](https://arxiv.org/abs/2401.05094)

* [Analysis of the $gg\to H\to hh\to4\tau$ process in the 2HDM lepton specific model at the LHC](https://arxiv.org/abs/2401.07289)

* [Machine Learning for Prediction of Unitarity and Bounded from Below Constraints](https://arxiv.org/abs/2401.09130) [[DOI](https://doi.org/10.22323/1.449.0494)]

* [Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the $4b$ Channel](https://arxiv.org/abs/2401.14198)

* [Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning](https://arxiv.org/abs/2402.10149)

* [Leptoquark Searches at TeV Scale Using Neural Networks at Hadron Collider](https://arxiv.org/abs/2405.08090)

* [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499)

* [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096)

* [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203)

####  Particle identification

* [Electromagnetic Showers Beyond Shower Shapes](https://arxiv.org/abs/1806.05667) [[DOI](https://doi.org/10.1016/j.nima.2019.162879)]

* [Survey of Machine Learning Techniques for High Energy Electromagnetic Shower Classification](https://dl4physicalsciences.github.io/files/nips_dlps_2017_24.pdf)

* [Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf)

* [The Full Event Interpretation}: {An Exclusive Tagging Algorithm for the Belle II Experiment](https://arxiv.org/abs/1807.08680) [[DOI](https://doi.org/10.1007/s41781-019-0021-8)]

* [Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics](https://arxiv.org/abs/1912.06794) [[DOI](https://doi.org/10.1140/epjc/s10052-020-8251-9)]

* [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://arxiv.org/abs/1902.07987) [[DOI](https://doi.org/10.1140/epjc/s10052-019-7113-9)]

* [Learning to Identify Electrons](https://arxiv.org/abs/2011.01984) [[DOI](https://doi.org/10.1103/PhysRevD.103.116028)]

* [Shower Identification in Calorimeter using Deep Learning](https://arxiv.org/abs/2103.16247)

* [A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme](https://arxiv.org/abs/2110.10259) [[DOI](https://doi.org/10.1088/1748-0221/17/02/P02018)]

* [Using Machine Learning for Particle Identification in ALICE](https://arxiv.org/abs/2204.06900) [[DOI](https://doi.org/10.1088/1748-0221/17/07/C07016)]

* [Artificial Intelligence for Imaging Cherenkov Detectors at the EIC](https://arxiv.org/abs/2204.08645) [[DOI](https://doi.org/10.1088/1748-0221/17/07/C07011)]

* [Using Artificial Intelligence in the Reconstruction of Signals from the PADME Electromagnetic Calorimeter](https://arxiv.org/abs/2210.00811) [[DOI](https://doi.org/10.3390/instruments6040046)]

* [Robust Neural Particle Identification Models](https://arxiv.org/abs/2212.07274) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012119)]

* [Separation of electrons from pions in GEM TRD using deep learning](https://arxiv.org/abs/2303.10776)

* [Machine learning method for $^{12}$C event classification and reconstruction in the active target time-projection chamber](https://arxiv.org/abs/2304.13233) [[DOI](https://doi.org/10.1016/j.nima.2023.168528)]

* [Inclusive, prompt and non-prompt $\rm{J}/\psi$ identification in proton-proton collisions at the Large Hadron Collider using machine learning](https://arxiv.org/abs/2308.00329) [[DOI](https://doi.org/10.1103/PhysRevD.109.014005)]

* [Particle-flow based tau identification at future $\textrm{e}^{+}\textrm{e}^{-}$ colliders](https://arxiv.org/abs/2307.07747) [[DOI](https://doi.org/10.1016/j.cpc.2024.109095)]

* [Identification of light leptons and pions in the electromagnetic calorimeter of Belle II](https://arxiv.org/abs/2301.05074) [[DOI](https://doi.org/10.1016/j.nima.2023.168630)]

* [Particle identification with the Belle II calorimeter using machine learning](https://arxiv.org/abs/2301.11654) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012111)]

* [Improved calorimetric particle identification in NA62 using machine learning techniques](https://arxiv.org/abs/2304.10580) [[DOI](https://doi.org/10.1007/JHEP11(2023)138)]

* [Particle identification with machine learning in ALICE Run 3](https://arxiv.org/abs/2309.07768) [[DOI](https://doi.org/10.1051/epjconf/202429509029)]

* [Study of residual artificial neural network for particle identification in the CEPC high-granularity calorimeter prototype](https://arxiv.org/abs/2310.09489) [[DOI](https://doi.org/10.1088/1748-0221/19/04/P04033)]

* [Machine-learning-based particle identification with missing data](https://arxiv.org/abs/2401.01905)

* [Detecting highly collimated photon-jets from Higgs boson exotic decays with deep learning](https://arxiv.org/abs/2401.15690)

####  Neutrino Detectors

* [A Convolutional Neural Network Neutrino Event Classifier](https://arxiv.org/abs/1604.01444) [[DOI](https://doi.org/10.1088/1748-0221/11/09/P09001)]

* [Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber](https://arxiv.org/abs/1611.05531) [[DOI](https://doi.org/10.1088/1748-0221/12/03/P03011)]

* [Convolutional Neural Networks for Electron Neutrino and Electron Shower Energy Reconstruction in the NO$\nu$A Detectors](https://dl4physicalsciences.github.io/files/nips_dlps_2017_7.pdf)

* [Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber](https://arxiv.org/abs/1808.07269) [[DOI](https://doi.org/10.1103/PhysRevD.99.092001)]

* [Scalable deep convolutional neural networks for sparse, locally dense liquid argon time projection chamber data](https://arxiv.org/abs/1903.05663) [[DOI](https://doi.org/10.1103/PhysRevD.102.012005)]

* [Event reconstruction for KM3NeT/ORCA using convolutional neural networks](https://arxiv.org/abs/2004.08254) [[DOI](https://doi.org/10.1088/1748-0221/15/10/P10005)]

* [PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics](https://arxiv.org/abs/2006.01993)

* [Point Proposal Network for Reconstructing 3D Particle Positions with Sub-Pixel Precision in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2006.14745) [[DOI](https://doi.org/10.1103/PhysRevD.104.032004)]

* [Neutrino interaction classification with a convolutional neural network in the DUNE far detector](https://arxiv.org/abs/2006.15052) [[DOI](https://doi.org/10.1103/PhysRevD.102.092003)]

* [Clustering of electromagnetic showers and particle interactions with graph neural networks in liquid argon time projection chambers](https://arxiv.org/abs/2007.01335) [[DOI](https://doi.org/10.1103/PhysRevD.104.072004)]

* [Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2007.03083)

* [Augmented signal processing in Liquid Argon Time Projection Chambers with a deep neural network](https://arxiv.org/abs/2007.12743) [[DOI](https://doi.org/10.1088/1748-0221/16/01/P01036)]

* [A Review on Machine Learning for Neutrino Experiments](https://arxiv.org/abs/2008.01242) [[DOI](https://doi.org/10.1142/S0217751X20430058)]

* [Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors](https://arxiv.org/abs/2009.00688) [[DOI](https://doi.org/10.1103/PhysRevD.103.032005)]

* [A Convolutional Neural Network for Multiple Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber](https://arxiv.org/abs/2010.08653) [[DOI](https://doi.org/10.1103/PhysRevD.103.092003)]

* [Study of using machine learning for level 1 trigger decision in JUNO experiment](https://arxiv.org/abs/2011.08847) [[DOI](https://doi.org/10.1109/TNS.2021.3085428)]

* [Deep-Learning-Based Kinematic Reconstruction for DUNE](https://arxiv.org/abs/2012.06181)

* [Semantic Segmentation with a Sparse Convolutional Neural Network for Event Reconstruction in MicroBooNE](https://arxiv.org/abs/2012.08513) [[DOI](https://doi.org/10.1103/PhysRevD.103.052012)]

* [Quantum Convolutional Neural Networks for High Energy Physics Data Analysis](https://arxiv.org/abs/2012.12177) [[DOI](https://doi.org/10.1103/PhysRevResearch.4.013231)]

* [Vertex and Energy Reconstruction in JUNO with Machine Learning Methods](https://arxiv.org/abs/2101.04839) [[DOI](https://doi.org/10.1016/j.nima.2021.165527)]

* [A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory](https://arxiv.org/abs/2101.11589) [[DOI](https://doi.org/10.1088/1748-0221/16/07/P07041)]

* [Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors](https://arxiv.org/abs/2102.01033)

* [Deep Learning strategies for ProtoDUNE raw data denoising](https://arxiv.org/abs/2103.01596) [[DOI](https://doi.org/10.1007/s41781-021-00077-9)]

* [Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2103.06233) [[DOI](https://doi.org/10.1051/epjconf/202125103054)]

* [A deep-learning based raw waveform region-of-interest finder for the liquid argon time projection chamber](https://arxiv.org/abs/2103.06391) [[DOI](https://doi.org/10.1088/1748-0221/17/01/P01018)]

* [Segmentation of EM showers for neutrino experiments with deep graph neural networks](https://arxiv.org/abs/2104.02040) [[DOI](https://doi.org/10.1088/1748-0221/16/12/P12035)]

* [CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors](https://arxiv.org/abs/2104.13426) [[DOI](https://doi.org/10.1088/1475-7516/2021/11/051)]

* [The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment](https://arxiv.org/abs/2106.02907) [[DOI](https://doi.org/10.1051/epjconf/202125103014)]

* [Deep learning reconstruction in ANTARES](https://arxiv.org/abs/2107.13654) [[DOI](https://doi.org/10.1088/1748-0221/16/09/C09018)]

* [Convolutional Neural Networks for Shower Energy Prediction in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2110.10766) [[DOI](https://doi.org/10.1088/1748-0221/17/02/P02022)]

* [Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $\pi^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE](https://arxiv.org/abs/2110.11874) [[DOI](https://doi.org/10.1088/1748-0221/16/12/T12017)]

* [Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation](https://arxiv.org/abs/2110.13961) [[DOI](https://doi.org/10.1088/1748-0221/17/01/P01037)]

* [Improvement of the NOvA Near Detector Event Reconstruction and Primary Vertexing through the Application of Machine Learning Methods](https://arxiv.org/abs/2112.01494)

* [Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network](https://arxiv.org/abs/2203.17053) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10791-2)]

* [Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2204.02496)

* [Application of Transfer Learning to Neutrino Interaction Classification](https://arxiv.org/abs/2207.03139) [[DOI](https://doi.org/10.1140/epjc/s10052-022-11066-6)]

* [Partition Pooling for Convolutional Graph Network Applications in Particle Physics](https://arxiv.org/abs/2208.05952) [[DOI](https://doi.org/10.1088/1748-0221/17/10/P10004)]

* [GraphNeT: Graph neural networks for neutrino telescope event reconstruction](https://arxiv.org/abs/2210.12194) [[DOI](https://doi.org/10.21105/joss.04971)]

* [Graph Neural Networks for low-energy event classification \& reconstruction in IceCube](https://arxiv.org/abs/2209.03042) [[DOI](https://doi.org/10.1088/1748-0221/17/11/P11003)]

* [Probing the mixing parameter |V\ensuremath{\tau}N|2 for heavy neutrinos](https://arxiv.org/abs/2211.00309) [[DOI](https://doi.org/10.1103/PhysRevD.107.095008)]

* [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744)

* [Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks](https://arxiv.org/abs/2303.08812) [[DOI](https://doi.org/10.22323/1.444.1004)]

* [Neutrino Reconstruction in TRIDENT Based on Graph Neural Network](https://arxiv.org/abs/2401.15324) [[DOI](https://doi.org/10.1007/978-981-97-0065-3_20)]

* [Using machine learning to separate Cherenkov and scintillation light in hybrid neutrino detector](https://arxiv.org/abs/2403.05184) [[DOI](https://doi.org/10.1088/1748-0221/19/04/P04027)]

* [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872)

* [Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore](https://arxiv.org/abs/2405.02163)

* [RELICS: a REactor neutrino LIquid xenon Coherent elastic Scattering experiment](https://arxiv.org/abs/2405.05554)

* [Improving Neutrino Energy Reconstruction with Machine Learning](https://arxiv.org/abs/2405.15867)

####  Direct Dark Matter Detectors

* Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment

* [Improving sensitivity to low-mass dark matter in LUX using a novel electrode background mitigation technique](https://arxiv.org/abs/2011.09602) [[DOI](https://doi.org/10.1103/PhysRevD.104.012011)]

* [Convolutional Neural Networks for Direct Detection of Dark Matter](https://arxiv.org/abs/1911.09210) [[DOI](https://doi.org/10.1088/1361-6471/ab8e94)]

* [Deep Learning for direct Dark Matter search with nuclear emulsions](https://arxiv.org/abs/2106.11995) [[DOI](https://doi.org/10.1016/j.cpc.2022.108312)]

* [Scanning the landscape of axion dark matter detectors: applying gradient descent to experimental design](https://arxiv.org/abs/2108.13894) [[DOI](https://doi.org/10.1103/PhysRevD.105.083010)]

* [Machine-learning techniques applied to three-year exposure of ANAIS-112](https://arxiv.org/abs/2110.10649) [[DOI](https://doi.org/10.1088/1742-6596/2156/1/012036)]

* [Signal-agnostic dark matter searches in direct detection data with machine learning](https://arxiv.org/abs/2110.12248) [[DOI](https://doi.org/10.1088/1475-7516/2022/02/039)]

* [Domain-informed neural networks for interaction localization within astroparticle experiments](https://arxiv.org/abs/2112.07995) [[DOI](https://doi.org/10.3389/frai.2022.832909)]

* [Improving the machine learning based vertex reconstruction for large liquid scintillator detectors with multiple types of PMTs](https://arxiv.org/abs/2205.04039) [[DOI](https://doi.org/10.1007/s41365-022-01078-y)]

* [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744)

* [Detector signal characterization with a Bayesian network in XENONnT](https://arxiv.org/abs/2304.05428) [[DOI](https://doi.org/10.1103/PhysRevD.108.012016)]

* [Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors](https://arxiv.org/abs/2403.15949)

* [Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection](https://arxiv.org/abs/2407.21008)

####  Cosmology, Astro Particle, and Cosmic Ray physics

* [Detecting Subhalos in Strong Gravitational Lens Images with Image Segmentation](https://arxiv.org/abs/2009.06663) [[DOI](https://doi.org/10.1051/0004-6361/202142030)]

* [Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning](https://arxiv.org/abs/1909.02005) [[DOI](https://doi.org/10.3847/1538-4357/ab4c41)]

* [Inverting cosmic ray propagation by Convolutional Neural Networks](https://arxiv.org/abs/2011.11930) [[DOI](https://doi.org/10.1088/1475-7516/2022/03/044)]

* [Particle Track Reconstruction using Geometric Deep Learning](https://arxiv.org/abs/2012.08515)

* [Deep-Learning based Reconstruction of the Shower Maximum $X_{\mathrm{max}}$ using the Water-Cherenkov Detectors of the Pierre Auger Observatory](https://arxiv.org/abs/2101.02946) [[DOI](https://doi.org/10.1088/1748-0221/16/07/P07019)]

* [A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications](https://arxiv.org/abs/2101.04525) [[DOI](https://doi.org/10.1007/JHEP05(2021)108)]

* [Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning](https://arxiv.org/abs/2101.11924)

* [Muon identification in a compact single-layered water Cherenkov detector and gamma/hadron discrimination using Machine Learning techniques](https://arxiv.org/abs/2101.10109) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09312-4)]

* [A convolutional-neural-network estimator of CMB constraints on dark matter energy injection](https://arxiv.org/abs/2101.10360) [[DOI](https://doi.org/10.1088/1475-7516/2021/06/025)]

* [A neural network classifier for electron identification on the DAMPE experiment](https://arxiv.org/abs/2102.05534) [[DOI](https://doi.org/10.1088/1748-0221/16/07/P07036)]

* [Bayesian nonparametric inference of neutron star equation of state via neural network](https://arxiv.org/abs/2103.05408) [[DOI](https://doi.org/10.3847/1538-4357/ac11f8)]

* [Novel null tests for the spatial curvature and homogeneity of the Universe and their machine learning reconstructions](https://arxiv.org/abs/2103.06789) [[DOI](https://doi.org/10.1103/PhysRevD.103.103539)]

* [Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations](https://arxiv.org/abs/2103.14039) [[DOI](https://doi.org/10.3847/2041-8213/ac09ef)]

* [Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning](https://arxiv.org/abs/2104.12789) [[DOI](https://doi.org/10.1093/mnras/stab3372)]

* [Development of Convolutional Neural Networks for an Electron-Tracking Compton Camera](https://arxiv.org/abs/2105.02512) [[DOI](https://doi.org/10.1093/ptep/ptab091)]

* [Machine Learning improved fits of the sound horizon at the baryon drag epoch](https://arxiv.org/abs/2106.00428) [[DOI](https://doi.org/10.1103/PhysRevD.104.043521)]

* [Using Convolutional Neural Networks for the Helicity Classification of Magnetic Fields](https://arxiv.org/abs/2106.06718) [[DOI](https://doi.org/10.22323/1.395.0906)]

* [Dim but not entirely dark: Extracting the Galactic Center Excess' source-count distribution with neural nets](https://arxiv.org/abs/2107.09070) [[DOI](https://doi.org/10.1103/PhysRevD.104.123022)]

* [Constraining dark matter annihilation with cosmic ray antiprotons using neural networks](https://arxiv.org/abs/2107.12395) [[DOI](https://doi.org/10.1088/1475-7516/2021/12/037)]

* [Probing Ultra-light Axion Dark Matter from 21cm Tomography using Convolutional Neural Networks](https://arxiv.org/abs/2108.07972) [[DOI](https://doi.org/10.1088/1475-7516/2022/01/020)]

* [Inferring dark matter substructure with astrometric lensing beyond the power spectrum](https://arxiv.org/abs/2110.01620) [[DOI](https://doi.org/10.1088/2632-2153/ac494a)]

* [A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess](https://arxiv.org/abs/2110.06931) [[DOI](https://doi.org/10.1103/PhysRevD.105.063017)]

* [Inference of cosmic-ray source properties by conditional invertible neural networks](https://arxiv.org/abs/2110.09493) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10138-x)]

* [Novel pre-burst stage of gamma-ray bursts from machine learning](https://arxiv.org/abs/1910.08043) [[DOI](https://doi.org/10.1016/j.jheap.2021.09.002)]

* [Deep learning techniques for Imaging Air Cherenkov Telescopes](https://arxiv.org/abs/2206.05296) [[DOI](https://doi.org/10.1103/PhysRevD.107.083026)]

* [Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation](https://arxiv.org/abs/2205.09126) [[DOI](https://doi.org/10.1093/mnras/stac3215)]

* [BlaST -- A Machine-Learning Estimator for the Synchrotron Peak of Blazars](https://arxiv.org/abs/2207.03813) [[DOI](https://doi.org/10.1016/j.ascom.2022.100646)]

* [Modeling early-universe energy injection with Dense Neural Networks](https://arxiv.org/abs/2207.06425) [[DOI](https://doi.org/10.1103/PhysRevD.107.063541)]

* [Cosmic Inflation and Genetic Algorithms](https://arxiv.org/abs/2208.13804) [[DOI](https://doi.org/10.1002/prop.202200161)]

* [Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation](https://arxiv.org/abs/2208.13796) [[DOI](https://doi.org/10.1093/mnras/stac3014)]

* [Uncovering dark matter density profiles in dwarf galaxies with graph neural networks](https://arxiv.org/abs/2208.12825) [[DOI](https://doi.org/10.1103/PhysRevD.107.043015)]

* [Progress in Nuclear Astrophysics: a multi-disciplinary field with still many open questions](https://arxiv.org/abs/2212.02156) [[DOI](https://doi.org/10.1088/1742-6596/2586/1/012104)]

* [Probing Cosmological Particle Production and Pairwise Hotspots with Deep Neural Networks](https://arxiv.org/abs/2303.08869) [[DOI](https://doi.org/10.1103/PhysRevD.108.043525)]

* [Nonparametric Model for the Equations of State of a Neutron Star from Deep Neural Network](https://arxiv.org/abs/2305.03323) [[DOI](https://doi.org/10.3847/1538-4357/acd335)]

* [Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks](https://arxiv.org/abs/2306.06929) [[DOI](https://doi.org/10.1103/PhysRevD.108.043031)]

* [Core States of Neutron Stars from Anatomizing Their Scaled Structure Equations](https://arxiv.org/abs/2306.08202) [[DOI](https://doi.org/10.3847/1538-4357/acdef0)]

* [A Deep Learning Approach to Extracting Nuclear Matter Properties from Neutron Star Observations](https://arxiv.org/abs/2303.17146) [[DOI](https://doi.org/10.3390/sym15051123)]

* [Sequential Monte Carlo with Cross-validated Neural Networks for Complexity of Hyperbolic Black Hole Solutions in 4D](https://arxiv.org/abs/2308.07907) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12284-2)]

* [Insights into neutron star equation of state by machine learning](https://arxiv.org/abs/2309.11227) [[DOI](https://doi.org/10.3847/1538-4357/ad2e8d)]

* [Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach](https://arxiv.org/abs/2401.07773) [[DOI](https://doi.org/10.3390/particles7010005)]

* [The Measurement and Modelling of Cosmic Ray Muons at KM3NeT Detectors](https://arxiv.org/abs/2402.02620)

* [Sibyll★](https://arxiv.org/abs/2404.02636) [[DOI](https://doi.org/10.1016/j.astropartphys.2024.102964)]

* [Preheating with deep learning](https://arxiv.org/abs/2405.08901)

* [Neural Networks Assisted Metropolis-Hastings for Bayesian Estimation of Critical Exponent on Elliptic Black Hole Solution in 4D Using Quantum Perturbation Theory](https://arxiv.org/abs/2406.04310)

* [Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy](https://arxiv.org/abs/2406.07395)

* [$\overline{\text{D}}$arkRayNet: Emulation of cosmic-ray antideuteron fluxes from dark matter](https://arxiv.org/abs/2406.18642)

####  Tracking

* [Particle Track Reconstruction with Deep Learning](https://dl4physicalsciences.github.io/files/nips_dlps_2017_28.pdf)

* [Novel deep learning methods for track reconstruction](https://arxiv.org/abs/1810.06111)

* [The Tracking Machine Learning challenge : Accuracy phase](https://arxiv.org/abs/1904.06778) [[DOI](https://doi.org/10.1007/978-3-030-29135-8\_9)]

* [Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors](https://arxiv.org/abs/2003.11603)

* [An updated hybrid deep learning algorithm for identifying and locating primary vertices](https://arxiv.org/abs/2007.01023)

* [Secondary Vertex Finding in Jets with Neural Networks](https://arxiv.org/abs/2008.02831) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09342-y)]

* [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://arxiv.org/abs/2007.00149)

* [First application of machine learning algorithms to the position reconstruction in Resistive Silicon Detectors](https://arxiv.org/abs/2011.02410) [[DOI](https://doi.org/10.1088/1748-0221/16/03/P03019)]

* [Beyond 4D Tracking: Using Cluster Shapes for Track Seeding](https://arxiv.org/abs/2012.04533) [[DOI](https://doi.org/10.1088/1748-0221/16/05/P05001)]

* [Hashing and metric learning for charged particle tracking](https://arxiv.org/abs/2101.06428)

* [Development of a Vertex Finding Algorithm using Recurrent Neural Network](https://arxiv.org/abs/2101.11906) [[DOI](https://doi.org/10.1016/j.nima.2022.167836)]

* [Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC](https://arxiv.org/abs/2103.00916) [[DOI](https://doi.org/10.1051/epjconf/202125103047)]

* [Progress in developing a hybrid deep learning algorithm for identifying and locating primary vertices](https://arxiv.org/abs/2103.04962) [[DOI](https://doi.org/10.1051/epjconf/202125104012)]

* [Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC](https://arxiv.org/abs/2103.06509)

* [Physics and Computing Performance of the Exa.TrkX TrackML Pipeline](https://arxiv.org/abs/2103.06995) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09675-8)]

* [Charged particle tracking via edge-classifying interaction networks](https://arxiv.org/abs/2103.16701) [[DOI](https://doi.org/10.1007/s41781-021-00073-z)]

* [Using Machine Learning to Select High-Quality Measurements](https://arxiv.org/abs/2106.08891) [[DOI](https://doi.org/10.1088/1748-0221/16/08/T08010)]

* [Optical Inspection of the Silicon Micro-strip Sensors for the CBM Experiment employing Artificial Intelligence](https://arxiv.org/abs/2107.07714) [[DOI](https://doi.org/10.1016/j.nima.2021.165932)]

* [Machine learning for surface prediction in ACTS](https://arxiv.org/abs/2108.03068) [[DOI](https://doi.org/10.1051/epjconf/202125103053)]

* [Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep Learning](https://arxiv.org/abs/2109.08982) [[DOI](https://doi.org/10.1063/5.0063300)]

* [Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline](https://arxiv.org/abs/2203.08800) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012117)]

* [Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field](https://arxiv.org/abs/2211.04890) [[DOI](https://doi.org/10.1038/s42005-023-01239-4)]

* [Deep learning for track recognition in pixel and strip-based particle detectors](https://arxiv.org/abs/2210.00599) [[DOI](https://doi.org/10.1088/1748-0221/17/12/P12023)]

* [Track Reconstruction using Geometric Deep Learning in the Straw Tube Tracker (STT) at the PANDA Experiment](https://arxiv.org/abs/2208.12178)

* [Fast muon tracking with machine learning implemented in FPGA](https://arxiv.org/abs/2202.04976) [[DOI](https://doi.org/10.1016/j.nima.2022.167546)]

* [Charged Particle Tracking with Machine Learning on FPGAs](https://arxiv.org/abs/2212.02348)

* [Reconstruction of fast neutron direction in segmented organic detectors using deep learning](https://arxiv.org/abs/2301.10796) [[DOI](https://doi.org/10.1016/j.nima.2023.168024)]

* [Deep Learning-Based Spatiotemporal Multi-Event Reconstruction for Delay Line Detectors](https://arxiv.org/abs/2306.09359) [[DOI](https://doi.org/10.1088/2632-2153/ad3d2d)]

* [Comparing and improving hybrid deep learning algorithms for identifying and locating primary vertices](https://arxiv.org/abs/2304.02423)

* [HyperTrack: Neural Combinatorics for High Energy Physics](https://arxiv.org/abs/2309.14113) [[DOI](https://doi.org/10.1051/epjconf/202429509021)]

* [Auto-tuning capabilities of the ACTS track reconstruction suite](https://arxiv.org/abs/2312.05123)

* [Ranking-based neural network for ambiguity resolution in ACTS](https://arxiv.org/abs/2312.05070) [[DOI](https://doi.org/10.1051/epjconf/202429503022)]

* [A Language Model for Particle Tracking](https://arxiv.org/abs/2402.10239)

* [Real-Time Charged Track Reconstruction for CLAS12](https://arxiv.org/abs/2403.04020)

* [Improving tracking algorithms with machine learning: a case for line-segment tracking at the High Luminosity LHC](https://arxiv.org/abs/2403.13166)

* [TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Era](https://arxiv.org/abs/2407.07179)

####  Heavy Ions / Nuclear Physics

* [An equation-of-state-meter of quantum chromodynamics transition from deep learning](https://arxiv.org/abs/1612.04262) [[DOI](https://doi.org/10.1038/s41467-017-02726-3)]

* [Probing heavy ion collisions using quark and gluon jet substructure](https://arxiv.org/abs/1803.03589)

* [Deep learning jet modifications in heavy-ion collisions](https://arxiv.org/abs/2012.07797) [[DOI](https://doi.org/10.1007/JHEP03(2021)206)]

* [Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning](https://arxiv.org/abs/1910.11530) [[DOI](https://doi.org/10.1140/epjc/s10052-020-8030-7)]

* [Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning](https://arxiv.org/abs/2103.01736) [[DOI](https://doi.org/10.1103/PhysRevD.103.094031)]

* [Constraining nuclear effects in Argon using machine learning algorithms](https://arxiv.org/abs/2105.12733)

* [Detecting Chiral Magnetic Effect via Deep Learning](https://arxiv.org/abs/2105.13761) [[DOI](https://doi.org/10.1103/PhysRevC.106.L051901)]

* [Classifying near-threshold enhancement using deep neural network](https://arxiv.org/abs/2106.03453) [[DOI](https://doi.org/10.1007/s00601-021-01642-z)]

* [Application of radial basis functions neutral networks in spectral functions](https://arxiv.org/abs/2106.08168) [[DOI](https://doi.org/10.1103/PhysRevD.104.076011)]

* [Deep Learning for the Classification of Quenched Jets](https://arxiv.org/abs/2106.08869) [[DOI](https://doi.org/10.1007/JHEP11(2021)219)]

* [inclusiveAI: A machine learning representation of the $F_2$ structure function over all charted $Q^2$ and $x$ range](https://arxiv.org/abs/2106.06390) [[DOI](https://doi.org/10.1103/PhysRevC.104.064321)]

* [Jet tomography in heavy ion collisions with deep learning](https://arxiv.org/abs/2106.11271) [[DOI](https://doi.org/10.1103/PhysRevLett.128.012301)]

* [An equation-of-state-meter for CBM using PointNet](https://arxiv.org/abs/2107.05590) [[DOI](https://doi.org/10.1007/JHEP10(2021)184)]

* [Probing criticality with deep learning in relativistic heavy-ion collisions](https://arxiv.org/abs/2107.11828) [[DOI](https://doi.org/10.1016/j.physletb.2022.137001)]

* [Modeling of charged-particle multiplicity and transverse-momentum distributions in pp collisions using a DNN](https://arxiv.org/abs/2108.06102) [[DOI](https://doi.org/10.1038/s41598-022-11618-6)]

* [Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions](https://arxiv.org/abs/2109.06277) [[DOI](https://doi.org/10.1103/PhysRevC.104.044902)]

* [Particles Multiplicity Based on Rapidity in Landau and Artificial Neural Network(ANN) Models](https://arxiv.org/abs/2109.07191) [[DOI](https://doi.org/10.1142/S0217751X22500026)]

* [Multiparton Interactions in pp collisions from Machine Learning](https://arxiv.org/abs/2110.01748) [[DOI](https://doi.org/10.22323/1.397.0347)]

* [Implementation of machine learning techniques to predict impact parameter and transverse spherocity in heavy-ion collisions at the LHC](https://arxiv.org/abs/2110.04026) [[DOI](https://doi.org/10.22323/1.397.0265)]

* [Deep Learning Exotic Hadrons](https://arxiv.org/abs/2110.13742) [[DOI](https://doi.org/10.1103/PhysRevD.105.L091501)]

* [Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies](https://arxiv.org/abs/2110.15026) [[DOI](https://doi.org/10.1088/1674-1137/ac5f9d)]

* [Studying Hadronization by Machine Learning Techniques](https://arxiv.org/abs/2111.15655)

* [The information content of jet quenching and machine learning assisted observable design](https://arxiv.org/abs/2111.14589) [[DOI](https://doi.org/10.1007/JHEP10(2022)011)]

* [Classification of quark and gluon jets in hot QCD medium with deep learning](https://arxiv.org/abs/2112.00681) [[DOI](https://doi.org/10.22323/1.380.0224)]

* [Jet tomography in hot QCD medium with deep learning](https://arxiv.org/abs/2112.00679) [[DOI](https://doi.org/10.22323/1.398.0302)]

* [Determination of impact parameter in high-energy heavy-ion collisions via deep learning](https://arxiv.org/abs/2112.03824) [[DOI](https://doi.org/10.1088/1674-1137/ac6490)]

* [Neural network reconstruction of the dense matter equation of state from neutron star observables](https://arxiv.org/abs/2201.01756) [[DOI](https://doi.org/10.1088/1475-7516/2022/08/071)]

* [Particle ratios with in Hadron Resonance Gas (HRG) and Artificial Neural Network (ANN) models](https://arxiv.org/abs/2201.04444)

* [New tool for kinematic regime estimation in semi-inclusive deep-inelastic scattering](https://arxiv.org/abs/2201.12197) [[DOI](https://doi.org/10.1007/JHEP04(2022)084)]

* [Efficient emulation of relativistic heavy ion collisions with transfer learning](https://arxiv.org/abs/2201.07302) [[DOI](https://doi.org/10.1103/PhysRevC.105.034910)]

* [Identifying quenched jets in heavy ion collisions with machine learning](https://arxiv.org/abs/2206.01628) [[DOI](https://doi.org/10.1007/JHEP04(2023)140)]

* [AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider](https://arxiv.org/abs/2205.09185) [[DOI](https://doi.org/10.1016/j.nima.2022.167748)]

* [Identify Hadronic Molecule States by Neural Network](https://arxiv.org/abs/2205.03572) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11170-1)]

* [Machine Learning model driven prediction of the initial geometry in Heavy-Ion Collision experiments](https://arxiv.org/abs/2203.15433) [[DOI](https://doi.org/10.1103/PhysRevC.106.014901)]

* [Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics](https://arxiv.org/abs/2210.06450) [[DOI](https://doi.org/10.1007/JHEP03(2023)085)]

* [Testing of KNO-scaling of charged hadron multiplicities within a Machine Learning based approach](https://arxiv.org/abs/2210.10548) [[DOI](https://doi.org/10.22323/1.414.1188)]

* [Optimization of the generator coordinate method with machine-learning techniques for nuclear spectra and neutrinoless double-\ensuremath{\beta} decay: Ridge regression for nuclei with axial deformation](https://arxiv.org/abs/2211.02797) [[DOI](https://doi.org/10.1103/PhysRevC.107.024304)]

* [A Kohn-Sham scheme based neural network for nuclear systems](https://arxiv.org/abs/2212.02093) [[DOI](https://doi.org/10.1016/j.physletb.2023.137870)]

* [Solving the nuclear pairing model with neural network quantum states](https://arxiv.org/abs/2211.04614) [[DOI](https://doi.org/10.1103/PhysRevE.107.025310)]

* [Deep-neural-network approach to solving the ab initio nuclear structure problem](https://arxiv.org/abs/2211.13998) [[DOI](https://doi.org/10.1103/PhysRevC.107.034320)]

* [Predicting \ensuremath{\beta}-decay energy with machine learning](https://arxiv.org/abs/2211.17136) [[DOI](https://doi.org/10.1103/PhysRevC.107.034308)]

* [Progress in Nuclear Astrophysics: a multi-disciplinary field with still many open questions](https://arxiv.org/abs/2212.02156) [[DOI](https://doi.org/10.1088/1742-6596/2586/1/012104)]

* [Estimating elliptic flow coefficient in heavy ion collisions using deep learning](https://arxiv.org/abs/2203.01246) [[DOI](https://doi.org/10.1103/PhysRevD.105.114022)]

* [Dilute neutron star matter from neural-network quantum states](https://arxiv.org/abs/2212.04436) [[DOI](https://doi.org/10.1103/PhysRevResearch.5.033062)]

* [Separating signal from combinatorial jets in a high background environment](https://arxiv.org/abs/2301.09148) [[DOI](https://doi.org/10.1103/PhysRevC.108.024909)]

* [Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies](https://arxiv.org/abs/2301.10426) [[DOI](https://doi.org/10.1103/PhysRevD.107.094001)]

* [Machine learning in nuclear physics at low and intermediate energies](https://arxiv.org/abs/2301.06396) [[DOI](https://doi.org/10.1007/s11433-023-2116-0)]

* [Bayesian inference of nucleus resonance and neutron skin](https://arxiv.org/abs/2301.07884) [[DOI](https://doi.org/10.7538/yzk.2022.youxian.0759)]

* [Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations](https://arxiv.org/abs/2304.03229) [[DOI](https://doi.org/10.1103/PhysRevC.109.034317)]

* [Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach](https://arxiv.org/abs/2304.08546) [[DOI](https://doi.org/10.3389/fphy.2023.1198572)]

* [Improving nuclear data evaluations with predictive reaction theory and indirect measurements](https://arxiv.org/abs/2304.10034) [[DOI](https://doi.org/10.1051/epjconf/202328403012)]

* [Deep learning for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2303.04517) [[DOI](https://doi.org/10.1103/PhysRevC.108.034905)]

* [Machine Learning based KNO-scaling of charged hadron multiplicities with Hijing++](https://arxiv.org/abs/2303.05422)

* [High energy nuclear physics meets Machine Learning](https://arxiv.org/abs/2303.06752) [[DOI](https://doi.org/10.1007/s41365-023-01233-z)]

* [Exploring QCD matter in extreme conditions with Machine Learning](https://arxiv.org/abs/2303.15136) [[DOI](https://doi.org/10.1016/j.ppnp.2023.104084)]

* [Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis](https://arxiv.org/abs/2304.07196) [[DOI](https://doi.org/10.21468/SciPostPhys.16.1.015)]

* [Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning](https://arxiv.org/abs/2305.00493) [[DOI](https://doi.org/10.1140/epja/s10050-023-01087-4)]

* [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852)

* [Nuclear corrections on the charged hadron fragmentation functions in a Neural Network global QCD analysis](https://arxiv.org/abs/2305.02664)

* [Demonstration of Sub-micron UCN Position Resolution using Room-temperature CMOS Sensor](https://arxiv.org/abs/2305.09562) [[DOI](https://doi.org/10.1016/j.nima.2023.168769)]

* [Predicting nuclear masses with product-unit networks](https://arxiv.org/abs/2305.04675) [[DOI](https://doi.org/10.1016/j.physletb.2024.138608)]

* [Neural network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) [[DOI](https://doi.org/10.1103/PhysRevC.107.065501)]

* [A machine learning study to identify collective flow in small and large colliding systems](https://arxiv.org/abs/2305.09937)

* [Machine learning transforms the inference of the nuclear equation of state](https://arxiv.org/abs/2305.16686) [[DOI](https://doi.org/10.1007/s11467-023-1313-3)]

* [Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning](https://arxiv.org/abs/2304.11930) [[DOI](https://doi.org/10.1088/2632-2153/acfd09)]

* [Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)](https://arxiv.org/abs/2306.04171) [[DOI](https://doi.org/10.1088/1674-1137/ad021c)]

* [Neutron-Gamma Pulse Shape Discrimination for Organic Scintillation Detector using 2D CNN based Image Classification](https://arxiv.org/abs/2306.09356)

* [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348)

* [IMSRG-Net: A machine learning-based solver for In-Medium Similarity Renormalization Group](https://arxiv.org/abs/2306.08878) [[DOI](https://doi.org/10.1103/PhysRevC.108.044303)]

* [Constraining the Woods-Saxon potential in fusion reactions based on a physics-informed neural network](https://arxiv.org/abs/2306.11236) [[DOI](https://doi.org/10.1103/PhysRevC.109.024601)]

* [Analysis of a Skyrme energy density functional with deep learning](https://arxiv.org/abs/2306.11314) [[DOI](https://doi.org/10.1103/PhysRevC.108.034311)]

* [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007)

* [Artificial Intelligence for the Electron Ion Collider (AI4EIC)](https://arxiv.org/abs/2307.08593) [[DOI](https://doi.org/10.1007/s41781-024-00113-4)]

* [Neural Network Solutions of Bosonic Quantum Systems in One Dimension](https://arxiv.org/abs/2309.02352) [[DOI](https://doi.org/10.1103/PhysRevC.109.034004)]

* [Neural Network Emulation of Spontaneous Fission](https://arxiv.org/abs/2310.01608) [[DOI](https://doi.org/10.1103/PhysRevC.109.044305)]

* [Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy](https://arxiv.org/abs/2401.08390)

* [Multiplicity Based Background Subtraction for Jets in Heavy Ion Collisions](https://arxiv.org/abs/2402.10945)

* [Deep learning for flow observables in high energy heavy-ion collisions](https://arxiv.org/abs/2404.02602)

* [A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider](https://arxiv.org/abs/2404.09839)

* [Pole structure of $P_\psi^N(4312)^+$ via machine learning and uniformized S-matrix](https://arxiv.org/abs/2405.11906)

* [Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2407.01338)

* [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163)

### Learning strategies

####  Hyperparameters

* [Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics](https://arxiv.org/abs/2011.04434) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08950-y)]

* [Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders](https://arxiv.org/abs/2109.08520) [[DOI](https://doi.org/10.22323/1.410.0012)]

* [Support vector machines and generalisation in HEP](https://arxiv.org/abs/1702.04686) [[DOI](https://doi.org/10.1088/1742-6596/898/7/072021)]

* [Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations](https://arxiv.org/abs/2306.11668)

* [Event Generator Tuning Incorporating Systematic Uncertainty](https://arxiv.org/abs/2310.07566) [[DOI](https://doi.org/10.1051/epjconf/202429506010)]

* [Auto-tuning capabilities of the ACTS track reconstruction suite](https://arxiv.org/abs/2312.05123)

####  Weak/Semi supervision

* [Weakly Supervised Classification in High Energy Physics](https://arxiv.org/abs/1702.00414) [[DOI](https://doi.org/10.1007/JHEP05(2017)145)]

* [Classification without labels: Learning from mixed samples in high energy physics](https://arxiv.org/abs/1708.02949) [[DOI](https://doi.org/10.1007/JHEP10(2017)174)]

* [Learning to classify from impure samples with high-dimensional data](https://arxiv.org/abs/1801.10158) [[DOI](https://doi.org/10.1103/PhysRevD.98.011502)]

* [Anomaly Detection for Resonant New Physics with Machine Learning](https://arxiv.org/abs/1805.02664) [[DOI](https://doi.org/10.1103/PhysRevLett.121.241803)]

* [Extending the search for new resonances with machine learning](https://arxiv.org/abs/1902.02634) [[DOI](https://doi.org/10.1103/PhysRevD.99.014038)]

* [Machine Learning on data with sPlot background subtraction](https://arxiv.org/abs/1905.11719) [[DOI](https://doi.org/10.1088/1748-0221/14/08/P08020)]

* [(Machine) Learning to Do More with Less](https://arxiv.org/abs/1706.09451) [[DOI](https://doi.org/10.1007/JHEP02(2018)034)]

* [An operational definition of quark and gluon jets](https://arxiv.org/abs/1809.01140) [[DOI](https://doi.org/10.1007/JHEP11(2018)059)]

* [Jet Topics: Disentangling Quarks and Gluons at Colliders](https://arxiv.org/abs/1802.00008) [[DOI](https://doi.org/10.1103/PhysRevLett.120.241602)]

* [Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector](https://arxiv.org/abs/2005.02983) [[DOI](https://doi.org/10.1103/PhysRevLett.125.131801)]

* [Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data](https://arxiv.org/abs/2002.12376) [[DOI](https://doi.org/10.1007/JHEP01(2021)153)]

* [Data-driven quark and gluon jet modification in heavy-ion collisions](https://arxiv.org/abs/2008.08596) [[DOI](https://doi.org/10.1103/PhysRevC.103.L021901)]

* [Machine learning approach for the search of resonances with topological features at the Large Hadron Collider](https://arxiv.org/abs/2011.09863) [[DOI](https://doi.org/10.1142/S0217751X21502419)]

* [Quark Gluon Jet Discrimination with Weakly Supervised Learning](https://arxiv.org/abs/2012.02540) [[DOI](https://doi.org/10.3938/jkps.75.652)]

* [An investigation of over-training within semi-supervised machine learning models in the search for heavy resonances at the LHC](https://arxiv.org/abs/2109.07287)

* [Disentangling Quarks and Gluons with CMS Open Data](https://arxiv.org/abs/2205.04459) [[DOI](https://doi.org/10.1103/PhysRevD.106.094021)]

* [Semi-supervised Graph Neural Networks for Pileup Noise Removal](https://arxiv.org/abs/2203.15823) [[DOI](https://doi.org/10.1140/epjc/s10052-022-11083-5)]

* [Boosting mono-jet searches with model-agnostic machine learning](https://arxiv.org/abs/2204.11889) [[DOI](https://doi.org/10.1007/JHEP08(2022)015)]

* [Going off topics to demix quark and gluon jets in \ensuremath{\alpha}$_{S}$ extractions](https://arxiv.org/abs/2206.10642) [[DOI](https://doi.org/10.1007/JHEP02(2023)150)]

* [TopicFlow: Disentangling quark and gluon jets with normalizing flows](https://arxiv.org/abs/2211.16053) [[DOI](https://doi.org/10.1103/PhysRevD.107.114003)]

* [Searching for dark jets with displaced vertices using weakly supervised machine learning](https://arxiv.org/abs/2305.04372) [[DOI](https://doi.org/10.1103/PhysRevD.108.035036)]

* [Learning to Isolate Muons in Data](https://arxiv.org/abs/2306.15737) [[DOI](https://doi.org/10.1103/PhysRevD.108.092008)]

* [Improving the performance of weak supervision searches using transfer and meta-learning](https://arxiv.org/abs/2312.06152) [[DOI](https://doi.org/10.1007/JHEP02(2024)138)]

* [Trials Factor for Semi-Supervised NN Classifiers in Searches for Narrow Resonances at the LHC](https://arxiv.org/abs/2404.07822)

####  Unsupervised

* [Fuzzy Jets](https://arxiv.org/abs/1509.02216) [[DOI](https://doi.org/10.1007/JHEP06(2016)010)]

* [Metric Space of Collider Events](https://arxiv.org/abs/1902.02346) [[DOI](https://doi.org/10.1103/PhysRevLett.123.041801)]

* [Learning the latent structure of collider events](https://arxiv.org/abs/2005.12319) [[DOI](https://doi.org/10.1007/JHEP10(2020)206)]

* [Uncovering latent jet substructure](https://arxiv.org/abs/1904.04200) [[DOI](https://doi.org/10.1103/PhysRevD.100.056002)]

* [Linearized Optimal Transport for Collider Events](https://arxiv.org/abs/2008.08604) [[DOI](https://doi.org/10.1103/PhysRevD.102.116019)]

* [Foundations of a Fast, Data-Driven, Machine-Learned Simulator](https://arxiv.org/abs/2101.08944) [[DOI](https://doi.org/10.1038/s41598-022-10966-7)]

* [Symmetries, Safety, and Self-Supervision](https://arxiv.org/abs/2108.04253) [[DOI](https://doi.org/10.21468/SciPostPhys.12.6.188)]

* [Unsupervised Domain Transfer for Science: Exploring Deep Learning Methods for Translation between LArTPC Detector Simulations with Differing Response Models](https://arxiv.org/abs/2304.12858)

* [NuCLR: Nuclear Co-Learned Representations](https://arxiv.org/abs/2306.06099)

* [A data-driven and model-agnostic approach to solving combinatorial assignment problems in searches for new physics](https://arxiv.org/abs/2309.05728) [[DOI](https://doi.org/10.1103/PhysRevD.109.L011702)]

* [Pre-training strategy using real particle collision data for event classification in collider physics](https://arxiv.org/abs/2312.06909)

* [PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction](https://arxiv.org/abs/2402.11538)

* [The Phase Space Distance Between Collider Events](https://arxiv.org/abs/2405.16698)

####  Reinforcement Learning

* [Jet grooming through reinforcement learning](https://arxiv.org/abs/1903.09644) [[DOI](https://doi.org/10.1103/PhysRevD.100.014014)]

* [Hierarchical clustering in particle physics through reinforcement learning](https://arxiv.org/abs/2011.08191)

* [Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster](https://arxiv.org/abs/2011.07371) [[DOI](https://doi.org/10.1103/PhysRevAccelBeams.24.104601)]

* [Particle Physics Model Building with Reinforcement Learning](https://arxiv.org/abs/2103.04759) [[DOI](https://doi.org/10.1007/JHEP08(2021)161)]

* [Reframing Jet Physics with New Computational Methods](https://arxiv.org/abs/2105.10512) [[DOI](https://doi.org/10.1051/epjconf/202125103059)]

* [A machine learning pipeline for autonomous numerical analytic continuation of Dyson-Schwinger equations](https://arxiv.org/abs/2112.13011) [[DOI](https://doi.org/10.1051/epjconf/202225809003)]

* [Simplifying Polylogarithms with Machine Learning](https://arxiv.org/abs/2206.04115) [[DOI](https://doi.org/10.1142/S2810939223500028)]

* [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176) [[DOI](https://doi.org/10.1007/JHEP12(2023)021)]

* [Lattice real-time simulations with learned optimal kernels](https://arxiv.org/abs/2310.08053) [[DOI](https://doi.org/10.1103/PhysRevD.109.L031502)]

* [Optimal operation of cryogenic calorimeters through deep reinforcement learning](https://arxiv.org/abs/2311.15147)

####  Quantum Machine Learning

* [Solving a Higgs optimization problem with quantum annealing for machine learning](https://doi.org/10.1038/nature24047)

* [Quantum adiabatic machine learning with zooming](https://arxiv.org/abs/1908.04480) [[DOI](https://doi.org/10.1103/PhysRevA.102.062405)]

* [Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier](https://arxiv.org/abs/2010.07335) [[DOI](https://doi.org/10.1007/JHEP02(2021)212)]

* [Event Classification with Quantum Machine Learning in High-Energy Physics](https://arxiv.org/abs/2002.09935) [[DOI](https://doi.org/10.1007/s41781-020-00047-7)]

* [Quantum Convolutional Neural Networks for High Energy Physics Data Analysis](https://arxiv.org/abs/2012.12177) [[DOI](https://doi.org/10.1103/PhysRevResearch.4.013231)]

* [Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits](https://arxiv.org/abs/2012.11560) [[DOI](https://doi.org/10.1088/1361-6471/ac1391)]

* [Quantum Machine Learning in High Energy Physics](https://arxiv.org/abs/2005.08582) [[DOI](https://doi.org/10.1088/2632-2153/abc17d)]

* [Hybrid Quantum-Classical Graph Convolutional Network](https://arxiv.org/abs/2101.06189)

* [Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers](https://arxiv.org/abs/2103.03897) [[DOI](https://doi.org/10.1007/JHEP08(2021)170)]

* [Quantum Support Vector Machines for Continuum Suppression in B Meson Decays](https://arxiv.org/abs/2103.12257) [[DOI](https://doi.org/10.1007/s41781-021-00075-x)]

* [Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC](https://arxiv.org/abs/2104.05059) [[DOI](https://doi.org/10.1103/PhysRevResearch.3.033221)]

* [Higgs analysis with quantum classifiers](https://arxiv.org/abs/2104.07692) [[DOI](https://doi.org/10.1051/epjconf/202125103070)]

* [Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States](https://arxiv.org/abs/2106.08334) [[DOI](https://doi.org/10.1007/JHEP08(2021)112)]

* [Style-based quantum generative adversarial networks for Monte Carlo events](https://arxiv.org/abs/2110.06933) [[DOI](https://doi.org/10.22331/q-2022-08-17-777)]

* [Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders](https://arxiv.org/abs/2111.07806)

* [Anomaly detection in high-energy physics using a quantum autoencoder](https://arxiv.org/abs/2112.04958) [[DOI](https://doi.org/10.1103/PhysRevD.105.095004)]

* [Quantum Machine Learning for $b$-jet identification](https://arxiv.org/abs/2202.13943) [[DOI](https://doi.org/10.1007/JHEP08(2022)014)]

* [Completely Quantum Neural Networks](https://arxiv.org/abs/2202.11727) [[DOI](https://doi.org/10.1103/PhysRevA.106.022601)]

* [Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data](https://arxiv.org/abs/2202.10471) [[DOI](https://doi.org/10.1103/PhysRevA.106.062423)]

* [Unsupervised Quantum Circuit Learning in High Energy Physics](https://arxiv.org/abs/2203.03578) [[DOI](https://doi.org/10.1103/PhysRevD.106.096006)]

* [Quantum Anomaly Detection for Collider Physics](https://arxiv.org/abs/2206.08391) [[DOI](https://doi.org/10.1007/JHEP02(2023)220)]

* [Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets](https://arxiv.org/abs/2211.03233) [[DOI](https://doi.org/10.3389/frai.2023.1268852)]

* [Quantum-probabilistic Hamiltonian learning for generative modelling \& anomaly detection](https://arxiv.org/abs/2211.03803) [[DOI](https://doi.org/10.1103/PhysRevA.108.062422)]

* [Reconstructing charged particle track segments with a quantum-enhanced support vector machine](https://arxiv.org/abs/2212.07279) [[DOI](https://doi.org/10.1103/PhysRevD.109.052002)]

* [Generative Invertible Quantum Neural Networks](https://arxiv.org/abs/2302.12906)

* [Quantum anomaly detection in the latent space of proton collision events at the LHC](https://arxiv.org/abs/2301.10780)

* [Unravelling physics beyond the standard model with classical and quantum anomaly detection](https://arxiv.org/abs/2301.10787) [[DOI](https://doi.org/10.1088/2632-2153/ad07f7)]

* [Precise Image Generation on Current Noisy Quantum Computing Devices](https://arxiv.org/abs/2307.05253) [[DOI](https://doi.org/10.1088/2058-9565/ad0389)]

* [Quantum Metric Learning for New Physics Searches at the LHC](https://arxiv.org/abs/2311.16866)

* [CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models](https://arxiv.org/abs/2312.03179)

* [Jet Discrimination with Quantum Complete Graph Neural Network](https://arxiv.org/abs/2403.04990)

* [New Pathways in Neutrino Physics via Quantum-Encoded Data Analysis](https://arxiv.org/abs/2402.19306)

####  Feature ranking

* [Mapping Machine-Learned Physics into a Human-Readable Space](https://arxiv.org/abs/2010.11998) [[DOI](https://doi.org/10.1103/PhysRevD.103.036020)]

* [Resurrecting $b\bar{b}h$ with kinematic shapes](https://arxiv.org/abs/2011.13945) [[DOI](https://doi.org/10.1007/JHEP04(2021)139)]

* [Feature Selection with Distance Correlation](https://arxiv.org/abs/2212.00046) [[DOI](https://doi.org/10.1103/PhysRevD.109.054009)]

####  Attention

* [Development of a Vertex Finding Algorithm using Recurrent Neural Network](https://arxiv.org/abs/2101.11906) [[DOI](https://doi.org/10.1016/j.nima.2022.167836)]

* [Learning the language of QCD jets with transformers](https://arxiv.org/abs/2303.07364) [[DOI](https://doi.org/10.1007/JHEP06(2023)184)]

* [Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models](https://arxiv.org/abs/2304.09208) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11809-z)]

* [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744)

* [Attention to Mean-Fields for Particle Cloud Generation](https://arxiv.org/abs/2305.15254)

####  Regularization

* [Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks](https://arxiv.org/abs/2102.01078) [[DOI](https://doi.org/10.1007/JHEP04(2021)296)]

* [Support vector machine classification on a biased training set: Multi-jet background rejection at hadron colliders](https://arxiv.org/abs/1407.0317) [[DOI](https://doi.org/10.1016/j.nima.2013.04.046)]

####  Optimal Transport

* [Metric Space of Collider Events](https://arxiv.org/abs/1902.02346) [[DOI](https://doi.org/10.1103/PhysRevLett.123.041801)]

* [Linearized Optimal Transport for Collider Events](https://arxiv.org/abs/2008.08604) [[DOI](https://doi.org/10.1103/PhysRevD.102.116019)]

* [Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders](https://arxiv.org/abs/2004.09360) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08891-6)]

* [Transport away your problems: Calibrating stochastic simulations with optimal transport](https://arxiv.org/abs/2107.08648) [[DOI](https://doi.org/10.1016/j.nima.2021.166119)]

* [Which Metric on the Space of Collider Events?](https://arxiv.org/abs/2111.03670) [[DOI](https://doi.org/10.1103/PhysRevD.105.076003)]

* [Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport](https://arxiv.org/abs/2208.02807)

* [Optimal transport for a global event description at high-intensity hadron colliders](https://arxiv.org/abs/2211.02029) [[DOI](https://doi.org/10.1103/PhysRevD.108.096003)]

* [Measurements of multijet event isotropies using optimal transport with the ATLAS detector](https://arxiv.org/abs/2305.16930) [[DOI](https://doi.org/10.1007/JHEP10(2023)060)]

* [Chained Quantile Morphing with Normalizing Flows](https://arxiv.org/abs/2309.15912)

### Fast inference / deployment

####  Software

* [On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics](https://arxiv.org/abs/2002.01427) [[DOI](https://doi.org/10.1088/2632-2153/ab983a)]

* [Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree](https://arxiv.org/abs/1210.6861) [[DOI](https://doi.org/10.1088/1748-0221/8/02/P02013)]

* [Deep topology classifiers for a more efficient trigger selection at the LHC](https://dl4physicalsciences.github.io/files/nips_dlps_2017_3.pdf)

* [Topology classification with deep learning to improve real-time event selection at the LHC](https://arxiv.org/abs/1807.00083) [[DOI](https://doi.org/10.1007/s41781-019-0028-1)]

* [Using holistic event information in the trigger](https://arxiv.org/abs/1808.00711)

* [Fast convolutional neural networks for identifying long-lived particles in a high-granularity calorimeter](https://arxiv.org/abs/2004.10744) [[DOI](https://doi.org/10.1088/1748-0221/15/12/P12006)]

* [A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications](https://arxiv.org/abs/2101.04525) [[DOI](https://doi.org/10.1007/JHEP05(2021)108)]

* [Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case](https://arxiv.org/abs/2103.10142) [[DOI](https://doi.org/10.5220/0010245002510258)]

* [Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities](https://arxiv.org/abs/2104.06622)

* [The Tracking Machine Learning challenge : Throughput phase](https://arxiv.org/abs/2105.01160) [[DOI](https://doi.org/10.1007/s41781-023-00094-w)]

* [Jet Single Shot Detection](https://arxiv.org/abs/2105.05785) [[DOI](https://doi.org/10.1051/epjconf/202125104027)]

* [Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep Learning](https://arxiv.org/abs/2109.08982) [[DOI](https://doi.org/10.1063/5.0063300)]

* [Event Classification with Multi-step Machine Learning](https://arxiv.org/abs/2106.02301) [[DOI](https://doi.org/10.1051/epjconf/202125103036)]

* [Deep machine learning for the PANDA software trigger](https://arxiv.org/abs/2211.15390) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11494-y)]

* [Exploration of different parameter optimization algorithms within the context of ACTS software framework](https://arxiv.org/abs/2211.00764)

* [FAIR AI Models in High Energy Physics](https://arxiv.org/abs/2212.05081) [[DOI](https://doi.org/10.1088/2632-2153/ad12e3)]

* [MLAnalysis: An open-source program for high energy physics analyses](https://arxiv.org/abs/2305.00964) [[DOI](https://doi.org/10.1016/j.cpc.2023.108957)]

* [Deep learning level-3 electron trigger for CLAS12](https://arxiv.org/abs/2302.07635) [[DOI](https://doi.org/10.1016/j.cpc.2023.108783)]

* [Data Preservation in High Energy Physics -- DPHEP Global Report 2022](https://arxiv.org/abs/2302.03583) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11885-1)]

* [Configurable calorimeter simulation for AI applications](https://arxiv.org/abs/2303.02101) [[DOI](https://doi.org/10.1088/2632-2153/acf186)]

* [Distilling particle knowledge for fast reconstruction at high-energy physics experiments](https://arxiv.org/abs/2311.12551) [[DOI](https://doi.org/10.1088/2632-2153/ad43b1)]

* [Machine Learning for Columnar High Energy Physics Analysis](https://arxiv.org/abs/2401.01802) [[DOI](https://doi.org/10.1051/epjconf/202429508011)]

* [Physics analysis for the HL-LHC: concepts and pipelines in practice with the Analysis Grand Challenge](https://arxiv.org/abs/2401.02766) [[DOI](https://doi.org/10.1051/epjconf/202429506016)]

* [Software Compensation for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2403.04632) [[DOI](https://doi.org/10.1088/1748-0221/19/04/P04037)]

* [RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation](https://arxiv.org/abs/2403.19330) [[DOI](https://doi.org/10.1051/epjconf/202429506019)]

* [Robust Independent Validation of Experiment and Theory: Rivet version 4 release note](https://arxiv.org/abs/2404.15984)

* [Implementing dynamic high-performance computing supported workflows on Scanning Transmission Electron Microscope](https://arxiv.org/abs/2406.11018)

####  Hardware/firmware

* [Fast inference of deep neural networks in FPGAs for particle physics](https://arxiv.org/abs/1804.06913) [[DOI](https://doi.org/10.1088/1748-0221/13/07/P07027)]

* [Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML](https://arxiv.org/abs/2003.06308) [[DOI](https://doi.org/10.1088/2632-2153/aba042)]

* [Fast inference of Boosted Decision Trees in FPGAs for particle physics](https://arxiv.org/abs/2002.02534) [[DOI](https://doi.org/10.1088/1748-0221/15/05/P05026)]

* [GPU coprocessors as a service for deep learning inference in high energy physics](https://arxiv.org/abs/2007.10359) [[DOI](https://doi.org/10.1088/2632-2153/abec21)]

* [Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics](https://arxiv.org/abs/2008.03601) [[DOI](https://doi.org/10.3389/fdata.2020.598927)]

* [Studying the potential of Graphcore IPUs for applications in Particle Physics](https://arxiv.org/abs/2008.09210) [[DOI](https://doi.org/10.1007/s41781-021-00057-z)]

* [PDFFlow: parton distribution functions on GPU](https://arxiv.org/abs/2009.06635) [[DOI](https://doi.org/10.1016/j.cpc.2021.107995)]

* [FPGAs-as-a-Service Toolkit (FaaST)](https://arxiv.org/abs/2010.08556) [[DOI](https://doi.org/10.1109/H2RC51942.2020.00010)]

* [Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs](https://arxiv.org/abs/2012.01563)

* [PDFFlow: hardware accelerating parton density access](https://arxiv.org/abs/2012.08221) [[DOI](https://doi.org/10.5821/zenodo.4286175)]

* [Fast convolutional neural networks on FPGAs with hls4ml](https://arxiv.org/abs/2101.05108) [[DOI](https://doi.org/10.1088/2632-2153/ac0ea1)]

* [Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference](https://arxiv.org/abs/2102.11289) [[DOI](https://doi.org/10.3389/frai.2021.676564)]

* [Sparse Deconvolution Methods for Online Energy Estimation in Calorimeters Operating in High Luminosity Conditions](https://arxiv.org/abs/2103.12467) [[DOI](https://doi.org/10.1088/1748-0221/16/09/P09008)]

* [Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics](https://arxiv.org/abs/2104.03408) [[DOI](https://doi.org/10.1088/1748-0221/16/08/P08016)]

* [A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC](https://arxiv.org/abs/2105.01683) [[DOI](https://doi.org/10.1109/TNS.2021.3087100)]

* [Muon trigger with fast Neural Networks on FPGA, a demonstrator](https://arxiv.org/abs/2105.04428) [[DOI](https://doi.org/10.1088/1742-6596/2374/1/012099)]

* [Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider](https://arxiv.org/abs/2108.03986) [[DOI](https://doi.org/10.1038/s42256-022-00441-3)]

* [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://arxiv.org/abs/2112.02048) [[DOI](https://doi.org/10.3389/fdata.2022.828666)]

* [Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors](https://arxiv.org/abs/2201.04740) [[DOI](https://doi.org/10.1109/NYSDS.2019.8909784)]

* [Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows](https://arxiv.org/abs/2202.09375) [[DOI](https://doi.org/10.21468/SciPostPhys.13.4.087)]

* [Fast muon tracking with machine learning implemented in FPGA](https://arxiv.org/abs/2202.04976) [[DOI](https://doi.org/10.1016/j.nima.2022.167546)]

* [Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml](https://arxiv.org/abs/2207.00559) [[DOI](https://doi.org/10.1088/2632-2153/acc0d7)]

* [Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics](https://arxiv.org/abs/2207.05602) [[DOI](https://doi.org/10.1088/1748-0221/17/09/P09039)]

* [Charged Particle Tracking with Machine Learning on FPGAs](https://arxiv.org/abs/2212.02348)

* [Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB](https://arxiv.org/abs/2212.07864) [[DOI](https://doi.org/10.1088/1748-0221/18/06/P06012)]

* [Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing](https://arxiv.org/abs/2301.04633) [[DOI](https://doi.org/10.1007/s41781-023-00101-0)]

* [Implementation of a framework for deploying AI inference engines in FPGAs](https://arxiv.org/abs/2305.19455)

* [Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliders](https://arxiv.org/abs/2307.05152) [[DOI](https://doi.org/10.1088/2632-2153/ad087a)]

* [Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics](https://arxiv.org/abs/2307.07289) [[DOI](https://doi.org/10.1007/s41781-024-00117-0)]

* [Tetris-inspired detector with neural network for radiation mapping](https://arxiv.org/abs/2302.07099) [[DOI](https://doi.org/10.1038/s41467-024-47338-w)]

* [Comparing machine learning models for tau triggers](https://arxiv.org/abs/2306.06743)

* [Development of the Topological Trigger for LHCb Run 3](https://arxiv.org/abs/2306.09873)

* [Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning](https://arxiv.org/abs/2310.02474)

* [Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection](https://arxiv.org/abs/2311.02038) [[DOI](https://doi.org/10.1051/epjconf/202429502033)]

* [Two Watts is All You Need: Enabling In-Detector Real-Time Machine Learning for Neutrino Telescopes Via Edge Computing](https://arxiv.org/abs/2311.04983)

* [Neural Network Methods for Radiation Detectors and Imaging](https://arxiv.org/abs/2311.05726) [[DOI](https://doi.org/10.3389/fphy.2024.1334298)]

* [Testing a Neural Network for Anomaly Detection in the CMS Global Trigger Test Crate during Run 3](https://arxiv.org/abs/2312.10009) [[DOI](https://doi.org/10.1088/1748-0221/19/03/C03029)]

* [Applications of Lipschitz neural networks to the Run 3 LHCb trigger system](https://arxiv.org/abs/2312.14265) [[DOI](https://doi.org/10.1051/epjconf/202429509005)]

* [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676)

* [Portable acceleration of CMS computing workflows with coprocessors as a service](https://arxiv.org/abs/2402.15366)

* [The Neural Network First-Level Hardware Track Trigger of the Belle II Experiment](https://arxiv.org/abs/2402.14962)

* [Comprehensive Machine Learning Model Comparison for Cherenkov and Scintillation Light Separation due to Particle Interactions](https://arxiv.org/abs/2406.09191)

* [Smart Pixels: In-pixel AI for on-sensor data filtering](https://arxiv.org/abs/2406.14860)

* [A Bayesian Framework to Investigate Radiation Reaction in Strong Fields](https://arxiv.org/abs/2406.19420)

* [Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes](https://arxiv.org/abs/2407.19010)

####  Deployment

* [MLaaS4HEP: Machine Learning as a Service for HEP](https://arxiv.org/abs/2007.14781) [[DOI](https://doi.org/10.1007/s41781-021-00061-3)]

* [Distributed training and scalability for the particle clustering method UCluster](https://arxiv.org/abs/2109.00264) [[DOI](https://doi.org/10.1051/epjconf/202125102054)]

* [Jet energy calibration with deep learning as a Kubeflow pipeline](https://arxiv.org/abs/2308.12724) [[DOI](https://doi.org/10.1007/s41781-023-00103-y)]

* [Optimizing High Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server](https://arxiv.org/abs/2312.06838)

* [Classifier Surrogates: Sharing AI-based Searches with the World](https://arxiv.org/abs/2402.15558)

* [HEP ML Lab: An end-to-end framework for applying machine learning into phenomenology studies](https://arxiv.org/abs/2405.02888)

##  Regression

###  Pileup

* [Pileup Mitigation with Machine Learning (PUMML)](https://arxiv.org/abs/1707.08600) [[DOI](https://doi.org/10.1007/JHEP12(2017)051)]

* [Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector](http://cds.cern.ch/record/2684070)

* [Pileup mitigation at the Large Hadron Collider with graph neural networks](https://arxiv.org/abs/1810.07988) [[DOI](https://doi.org/10.1140/epjp/i2019-12710-3)]

* [Jet grooming through reinforcement learning](https://arxiv.org/abs/1903.09644) [[DOI](https://doi.org/10.1103/PhysRevD.100.014014)]

* [Pile-Up Mitigation using Attention](https://arxiv.org/abs/2107.02779) [[DOI](https://doi.org/10.1088/2632-2153/ac7198)]

* [Semi-supervised Graph Neural Networks for Pileup Noise Removal](https://arxiv.org/abs/2203.15823) [[DOI](https://doi.org/10.1140/epjc/s10052-022-11083-5)]

* [Towards an automated data cleaning with deep learning in CRESST](https://arxiv.org/abs/2211.00564) [[DOI](https://doi.org/10.1140/epjp/s13360-023-03674-2)]

* [Restoring original signals from pile-up using deep learning](https://arxiv.org/abs/2304.14496) [[DOI](https://doi.org/10.1016/j.nima.2023.168492)]

* [High Pileup Particle Tracking with Object Condensation](https://arxiv.org/abs/2312.03823)

###  Calibration

* [Parametrizing the Detector Response with Neural Networks](https://arxiv.org/abs/1910.03773) [[DOI](https://doi.org/10.1088/1748-0221/15/01/P01030)]

* [Simultaneous Jet Energy and Mass Calibrations with Neural Networks](http://cds.cern.ch/record/2706189)

* [Generalized Numerical Inversion: A Neural Network Approach to Jet Calibration](http://cds.cern.ch/record/2630972)

* [Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf)

* [Per-Object Systematics using Deep-Learned Calibration](https://arxiv.org/abs/2003.11099) [[DOI](https://doi.org/10.21468/SciPostPhys.9.6.089)]

* [A deep neural network for simultaneous estimation of b jet energy and resolution](https://arxiv.org/abs/1912.06046) [[DOI](https://doi.org/10.1007/s41781-020-00041-z)]

* [How to GAN Higher Jet Resolution](https://arxiv.org/abs/2012.11944) [[DOI](https://doi.org/10.21468/SciPostPhys.13.3.064)]

* [Deep learning jet modifications in heavy-ion collisions](https://arxiv.org/abs/2012.07797) [[DOI](https://doi.org/10.1007/JHEP03(2021)206)]

* [Calorimetric Measurement of Multi-TeV Muons via Deep Regression](https://arxiv.org/abs/2107.02119) [[DOI](https://doi.org/10.1140/epjc/s10052-022-09993-5)]

* [Transport away your problems: Calibrating stochastic simulations with optimal transport](https://arxiv.org/abs/2107.08648) [[DOI](https://doi.org/10.1016/j.nima.2021.166119)]

* [On the Use of Neural Networks for Energy Reconstruction in High-granularity Calorimeters](https://arxiv.org/abs/2107.10207) [[DOI](https://doi.org/10.1088/1748-0221/16/12/P12036)]

* [Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data](https://arxiv.org/abs/2002.03605) [[DOI](https://doi.org/10.1140/epjc/s10052-020-08461-2)]

* [Perspectives on the Calibration of CNN Energy Reconstruction in Highly Granular Calorimeters](https://arxiv.org/abs/2108.10963)

* [Deeply Learning Deep Inelastic Scattering Kinematics](https://arxiv.org/abs/2108.11638) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10964-z)]

* [Energy reconstruction in a liquid argon calorimeter cell using convolutional neural networks](https://arxiv.org/abs/2109.05124) [[DOI](https://doi.org/10.1088/1748-0221/17/01/P01002)]

* [Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos](https://arxiv.org/abs/2109.08152) [[DOI](https://doi.org/10.1088/1748-0221/16/09/C09019)]

* [Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning](https://arxiv.org/abs/2110.05505) [[DOI](https://doi.org/10.1016/j.nima.2021.166164)]

* [Implicit Quantile Neural Networks for Jet Simulation and Correction](https://arxiv.org/abs/2111.11415)

* [Reconstructing partonic kinematics at colliders with Machine Learning](https://arxiv.org/abs/2112.05043) [[DOI](https://doi.org/10.21468/SciPostPhysCore.5.4.049)]

* [Machine Learning for Particle Flow Reconstruction at CMS](https://arxiv.org/abs/2203.00330) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012100)]

* [Machine-learning-based prediction of parameters of secondaries in hadronic showers using calorimetric observables](https://arxiv.org/abs/2205.12534) [[DOI](https://doi.org/10.1088/1748-0221/17/10/P10031)]

* [Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm](https://arxiv.org/abs/2203.02841)

* [Reconstruction of Missing Resonances Combining Nearest Neighbors Regressors and Neural Network Classifiers](https://arxiv.org/abs/2203.03662) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10714-1)]

* [A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer](https://arxiv.org/abs/2203.05687) [[DOI](https://doi.org/10.1103/PhysRevD.107.114029)]

* [Deep learning applications for quality control in particle detector construction](https://arxiv.org/abs/2203.08969)

* [Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics](https://arxiv.org/abs/2205.03413) [[DOI](https://doi.org/10.1103/PhysRevLett.129.082001)]

* [Bias and Priors in Machine Learning Calibrations for High Energy Physics](https://arxiv.org/abs/2205.05084) [[DOI](https://doi.org/10.1103/PhysRevD.106.036011)]

* [Deep learning techniques for energy clustering in the CMS ECAL](https://arxiv.org/abs/2204.10277) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012077)]

* [$\nu$-Flows: conditional neutrino regression](https://arxiv.org/abs/2207.00664) [[DOI](https://doi.org/10.21468/SciPostPhys.14.6.159)]

* [Machine Learned Particle Detector Simulations](https://arxiv.org/abs/2207.11254)

* [A new method for the $q^2$ reconstruction in semileptonic decays at LHCb based on machine learning](https://arxiv.org/abs/2208.02145) [[DOI](https://doi.org/10.1155/2023/8127604)]

* [Machine learning approaches for parameter reweighting for MC samples of top quark production in CMS](https://arxiv.org/abs/2211.07355) [[DOI](https://doi.org/10.22323/1.414.1045)]

* [Firmware implementation of a recurrent neural network for the computation of the energy deposited in the liquid argon calorimeter of the ATLAS experiment](https://arxiv.org/abs/2302.07555) [[DOI](https://doi.org/10.1088/1748-0221/18/05/P05017)]

* [Restoring the saturation response of a PMT using pulse shape and artificial neural networks](https://arxiv.org/abs/2302.06170) [[DOI](https://doi.org/10.1093/ptep/ptad047)]

* [A neural network for beam background decomposition in Belle~II at SuperKEKB](https://arxiv.org/abs/2301.06170) [[DOI](https://doi.org/10.1016/j.nima.2023.168112)]

* [Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning](https://arxiv.org/abs/2305.00493) [[DOI](https://doi.org/10.1140/epja/s10050-023-01087-4)]

* [A fast and flexible machine learning approach to data quality monitoring](https://arxiv.org/abs/2301.08917)

* [Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test](https://arxiv.org/abs/2303.05413) [[DOI](https://doi.org/10.1088/2632-2153/acebb7)]

* [Nuclear corrections on the charged hadron fragmentation functions in a Neural Network global QCD analysis](https://arxiv.org/abs/2305.02664)

* [$\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://arxiv.org/abs/2307.02405) [[DOI](https://doi.org/10.1103/PhysRevD.109.012005)]

* [Removing Noise From Simulated Events at The Main Drift Chamber of BESIII Using Convolutional Neural Networks](https://arxiv.org/abs/2303.12202)

* [New techniques for jet calibration with the ATLAS detector](https://arxiv.org/abs/2303.17312) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11837-9)]

* [Correction of the baseline fluctuations in the GEM-based ALICE TPC](https://arxiv.org/abs/2304.03881) [[DOI](https://doi.org/10.1088/1748-0221/18/11/P11021)]

* [A first application of machine and deep learning for background rejection in the ALPS II TES detector](https://arxiv.org/abs/2304.08406) [[DOI](https://doi.org/10.1002/andp.202200545)]

* [Jet energy calibration with deep learning as a Kubeflow pipeline](https://arxiv.org/abs/2308.12724) [[DOI](https://doi.org/10.1007/s41781-023-00103-y)]

* [Refining fast simulation using machine learning](https://arxiv.org/abs/2309.12919) [[DOI](https://doi.org/10.1051/epjconf/202429509032)]

* [The Optimal use of Segmentation for Sampling Calorimeters](https://arxiv.org/abs/2310.04442)

* [Using deep neural networks to improve the precision of fast-sampled particle timing detectors](https://arxiv.org/abs/2312.05883) [[DOI](https://doi.org/10.7494/csci.2024.25.1.5784)]

* [Machine learning based event reconstruction for the MUonE experiment](https://arxiv.org/abs/2402.02913) [[DOI](https://doi.org/10.7494/csci.2024.25.1.5690)]

* [A Comparison of Deep Learning Models for Proton Background Rejection with the AMS Electromagnetic Calorimeter](https://arxiv.org/abs/2402.16285)

* [ML-based Calibration and Control of the GlueX Central Drift Chamber](https://arxiv.org/abs/2403.13823)

###  Recasting

* [The BSM-AI project: SUSY-AI--generalizing LHC limits on supersymmetry with machine learning](https://arxiv.org/abs/1605.02797) [[DOI](https://doi.org/10.1140/epjc/s10052-017-4814-9)]

* [Accelerating the BSM interpretation of LHC data with machine learning](https://arxiv.org/abs/1611.02704) [[DOI](https://doi.org/10.1016/j.dark.2019.100293)]

* [Bayesian Neural Networks for Fast SUSY Predictions](https://arxiv.org/abs/2007.04506) [[DOI](https://doi.org/10.1016/j.physletb.2020.136041)]

* [Exploration of Parameter Spaces Assisted by Machine Learning](https://arxiv.org/abs/2207.09959) [[DOI](https://doi.org/10.1016/j.cpc.2023.108902)]

* [HackAnalysis 2: A powerful and hackable recasting tool](https://arxiv.org/abs/2406.10042)

###  Matrix elements

* [Using neural networks for efficient evaluation of high multiplicity scattering amplitudes](https://arxiv.org/abs/2002.07516) [[DOI](https://doi.org/10.1007/JHEP06(2020)114)]

* [(Machine) Learning Amplitudes for Faster Event Generation](https://arxiv.org/abs/1912.11055) [[DOI](https://doi.org/10.1103/PhysRevD.107.L071901)]

* [$\textsf{Xsec}$: the cross-section evaluation code](https://arxiv.org/abs/2006.16273) [[DOI](https://doi.org/10.1140/epjc/s10052-020-08635-y)]

* [Matrix Element Regression with Deep Neural Networks -- breaking the CPU barrier](https://arxiv.org/abs/2008.10949) [[DOI](https://doi.org/10.1007/JHEP04(2021)020)]

* [Unveiling the pole structure of S-matrix using deep learning](https://arxiv.org/abs/2104.14182) [[DOI](https://doi.org/10.31349/SuplRevMexFis.3.0308067)]

* [Model independent analysis of coupled-channel scattering: a deep learning approach](https://arxiv.org/abs/2105.04898) [[DOI](https://doi.org/10.1103/PhysRevD.104.036001)]

* [Optimising simulations for diphoton production at hadron colliders using amplitude neural networks](https://arxiv.org/abs/2106.09474) [[DOI](https://doi.org/10.1007/JHEP08(2021)066)]

* [A factorisation-aware Matrix element emulator](https://arxiv.org/abs/2107.06625) [[DOI](https://doi.org/10.1007/JHEP11(2021)066)]

* [Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates](https://arxiv.org/abs/2109.11964) [[DOI](https://doi.org/10.21468/SciPostPhys.12.5.164)]

* [Targeting Multi-Loop Integrals with Neural Networks](https://arxiv.org/abs/2112.09145) [[DOI](https://doi.org/10.21468/SciPostPhys.12.4.129)]

* [Fast and precise model calculation for KATRIN using a neural network](https://arxiv.org/abs/2201.04523) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10384-z)]

* [SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine ALearning](https://arxiv.org/abs/2206.08901) [[DOI](https://doi.org/10.1088/2632-2153/acb2b2)]

* [Simplifying Polylogarithms with Machine Learning](https://arxiv.org/abs/2206.04115) [[DOI](https://doi.org/10.1142/S2810939223500028)]

* [Loop Amplitudes from Precision Networks](https://arxiv.org/abs/2206.14831) [[DOI](https://doi.org/10.21468/SciPostPhysCore.6.2.034)]

* [Unweighting multijet event generation using factorisation-aware neural networks](https://arxiv.org/abs/2301.13562) [[DOI](https://doi.org/10.21468/SciPostPhys.15.3.107)]

* [One-loop matrix element emulation with factorisation awareness](https://arxiv.org/abs/2302.04005) [[DOI](https://doi.org/10.1007/JHEP05(2023)159)]

* [Pole-fitting for complex functions: Enhancing standard techniques by artificial-neural-network classifiers and regressors](https://arxiv.org/abs/2309.08358) [[DOI](https://doi.org/10.1016/j.cpc.2023.108998)]

* [The MadNIS Reloaded](https://arxiv.org/abs/2311.01548)

###  Parameter estimation

* [Numerical analysis of neutrino physics within a high scale supersymmetry model via machine learning](https://arxiv.org/abs/2006.01495) [[DOI](https://doi.org/10.1142/S0217732320502181)]

* [Parametrized classifiers for optimal EFT sensitivity](https://arxiv.org/abs/2007.10356) [[DOI](https://doi.org/10.1007/JHEP05(2021)247)]

* [MCNNTUNES: tuning Shower Monte Carlo generators with machine learning](https://arxiv.org/abs/2010.02213) [[DOI](https://doi.org/10.1016/j.cpc.2021.107908)]

* [Deep-Learned Event Variables for Collider Phenomenology](https://arxiv.org/abs/2105.10126) [[DOI](https://doi.org/10.1103/PhysRevD.107.L031904)]

* [Using Machine Learning techniques in phenomenological studies in flavour physics](https://arxiv.org/abs/2109.07405) [[DOI](https://doi.org/10.1007/JHEP07(2022)115)]

* [Machine learning a manifold](https://arxiv.org/abs/2112.07673) [[DOI](https://doi.org/10.1103/PhysRevD.105.096030)]

* [LHC EFT WG Report: Experimental Measurements and Observables](https://arxiv.org/abs/2211.08353)

* [Machine Learning Assisted Vector Atomic Magnetometry](https://arxiv.org/abs/2301.05707) [[DOI](https://doi.org/10.1038/s41467-023-41676-x)]

* [Exploration of different parameter optimization algorithms within the context of ACTS software framework](https://arxiv.org/abs/2211.00764)

* [Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models](https://arxiv.org/abs/2304.09208) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11809-z)]

* [Neural network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217) [[DOI](https://doi.org/10.1103/PhysRevC.107.065501)]

* [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852)

* [Determination of high-energy hadronic interaction properties from observables of proton initiated extensive air showers](https://arxiv.org/abs/2304.08007)

* [Improving the temporal resolution of event-based electron detectors using neural network cluster analysis](https://arxiv.org/abs/2307.16666) [[DOI](https://doi.org/10.1016/j.ultramic.2023.113881)]

* [First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector](https://arxiv.org/abs/2310.06281) [[DOI](https://doi.org/10.1103/PhysRevD.109.052005)]

* [Training Deep 3D Convolutional Neural Networks to Extract BSM Physics Parameters Directly from HEP Data: a Proof-of-Concept Study Using Monte Carlo Simulations](https://arxiv.org/abs/2311.13060)

* [Reconstruction of electromagnetic showers in calorimeters using Deep Learning](https://arxiv.org/abs/2311.17914)

###  Parton Distribution Functions (and related)

* [Neural-network analysis of Parton Distribution Functions from Ioffe-time pseudodistributions](https://arxiv.org/abs/2010.03996) [[DOI](https://doi.org/10.1007/JHEP02(2021)138)]

* [Deep Learning Analysis of Deeply Virtual Exclusive Photoproduction](https://arxiv.org/abs/2012.04801) [[DOI](https://doi.org/10.1103/PhysRevD.104.016001)]

* [PDFFlow: hardware accelerating parton density access](https://arxiv.org/abs/2012.08221) [[DOI](https://doi.org/10.5821/zenodo.4286175)]

* [Compressing PDF sets using generative adversarial networks](https://arxiv.org/abs/2104.04535) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09338-8)]

* [The Path to Proton Structure at One-Percent Accuracy](https://arxiv.org/abs/2109.02653) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10328-7)]

* [An open-source machine learning framework for global analyses of parton distributions](https://arxiv.org/abs/2109.02671) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09747-9)]

* [Exploring the substructure of nucleons and nuclei with machine learning](https://arxiv.org/abs/2110.01924)

* [A new generation of simultaneous fits to LHC data using deep learning](https://arxiv.org/abs/2201.07240) [[DOI](https://doi.org/10.1007/JHEP05(2022)032)]

* [Unpolarized proton PDF at NNLO from lattice QCD with physical quark masses](https://arxiv.org/abs/2212.12569) [[DOI](https://doi.org/10.1103/PhysRevD.107.074509)]

* [Simultaneous CTEQ-TEA extraction of PDFs and SMEFT parameters from jet and $ t\overline{t} $ data](https://arxiv.org/abs/2211.01094) [[DOI](https://doi.org/10.1007/JHEP05(2023)003)]

* [Neutrino Structure Functions from GeV to EeV Energies](https://arxiv.org/abs/2302.08527) [[DOI](https://doi.org/10.1007/JHEP05(2023)149)]

* [Determination of the distribution of strong coupling constant with machine learning](https://arxiv.org/abs/2303.07968)

* [The top quark legacy of the LHC Run II for PDF and SMEFT analyses](https://arxiv.org/abs/2303.06159) [[DOI](https://doi.org/10.1007/JHEP05(2023)205)]

* [Research on the distribution formula of QCD strong coupling constant in medium and high energy scale region based on symbolic regression algorithm](https://arxiv.org/abs/2304.07682) [[DOI](https://doi.org/10.1088/0256-307X/41/3/031201)]

* [A Modern Global Extraction of the Sivers Function](https://arxiv.org/abs/2304.14328) [[DOI](https://doi.org/10.1103/PhysRevD.108.054007)]

* [Towards an integrated determination of proton, deuteron and nuclear PDFs](https://arxiv.org/abs/2307.05967)

* [Learning PDFs through Interpretable Latent Representations in Mellin Space](https://arxiv.org/abs/2312.02278)

* [Photons in the proton: implications for the LHC](https://arxiv.org/abs/2401.08749)

* [Determination of the theory uncertainties from missing higher orders on NNLO parton distributions with percent accuracy](https://arxiv.org/abs/2401.10319)

* [Unraveling generalized parton distributions through Lorentz symmetry and partial DGLAP knowledge](https://arxiv.org/abs/2401.12013) [[DOI](https://doi.org/10.1103/PhysRevD.109.096013)]

* [Using Machine Learning to Improve PDF Uncertainties](https://arxiv.org/abs/2401.13050)

* [SIMUnet: an open-source tool for simultaneous global fits of EFT Wilson coefficients and PDFs](https://arxiv.org/abs/2402.03308)

* [Helicity-dependent parton distribution functions at next-to-next-to-leading order accuracy from inclusive and semi-inclusive deep-inelastic scattering data](https://arxiv.org/abs/2404.04712)

* [Determination of $K^0_S$ Fragmentation Functions including BESIII Measurements and using Neural Networks](https://arxiv.org/abs/2404.07334)

* [Using analytic models to describe effective PDFs](https://arxiv.org/abs/2404.15175)

* [NNPDF4.0 aN$^3$LO PDFs with QED corrections](https://arxiv.org/abs/2406.01779)

* [A generalized statistical model for fits to parton distributions](https://arxiv.org/abs/2406.01664)

* [Extraction of Information from Polarized Deep Exclusive Scattering with Machine Learning](https://arxiv.org/abs/2406.09258)

* [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411)

###  Lattice Gauge Theory

* [Equivariant flow-based sampling for lattice gauge theory](https://arxiv.org/abs/2003.06413) [[DOI](https://doi.org/10.1103/PhysRevLett.125.121601)]

* [Lattice gauge equivariant convolutional neural networks](https://arxiv.org/abs/2012.12901) [[DOI](https://doi.org/10.1103/PhysRevLett.128.032003)]

* [Generalization capabilities of translationally equivariant neural networks](https://arxiv.org/abs/2103.14686) [[DOI](https://doi.org/10.1103/PhysRevD.104.074504)]

* [Heavy Quark Potential in QGP: DNN meets LQCD](https://arxiv.org/abs/2105.07862) [[DOI](https://doi.org/10.1103/PhysRevD.105.014017)]

* [Flow-based sampling for multimodal distributions in lattice field theory](https://arxiv.org/abs/2107.00734)

* [Machine Learning Estimators for Lattice QCD Observables](https://arxiv.org/abs/1807.05971) [[DOI](https://doi.org/10.1103/PhysRevD.100.014504)]

* [Machine-learning prediction for quasiparton distribution function matrix elements](https://arxiv.org/abs/1909.10990) [[DOI](https://doi.org/10.1103/PhysRevD.101.034516)]

* [A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer](https://arxiv.org/abs/1911.06267) [[DOI](https://doi.org/10.1038/s41598-020-67769-x)]

* [Lattice gauge symmetry in neural networks](https://arxiv.org/abs/2111.04389) [[DOI](https://doi.org/10.22323/1.396.0185)]

* [Machine learning Hadron Spectral Functions in Lattice QCD](https://arxiv.org/abs/2112.00460) [[DOI](https://doi.org/10.22323/1.396.0148)]

* [Equivariance and generalization in neural networks](https://arxiv.org/abs/2112.12493) [[DOI](https://doi.org/10.1051/epjconf/202225809001)]

* [Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help](https://arxiv.org/abs/2201.02564) [[DOI](https://doi.org/10.1016/j.cpc.2022.108547)]

* [Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation](https://arxiv.org/abs/2211.03198)

* [Fourier-flow model generating Feynman paths](https://arxiv.org/abs/2211.03470) [[DOI](https://doi.org/10.1103/PhysRevD.107.056001)]

* [Deep-learning quasi-particle masses from QCD equation of state](https://arxiv.org/abs/2211.07994) [[DOI](https://doi.org/10.1016/j.physletb.2023.138088)]

* [Massive gauge theory with quasigluon for hot SU(N): Phase transition and thermodynamics](https://arxiv.org/abs/2211.09442) [[DOI](https://doi.org/10.1103/PhysRevD.107.076005)]

* [Learning trivializing flows](https://arxiv.org/abs/2211.12806) [[DOI](https://doi.org/10.22323/1.430.0001)]

* [Gluon helicity distribution in the nucleon from lattice QCD and machine learning](https://arxiv.org/abs/2211.15587) [[DOI](https://doi.org/10.1103/PhysRevD.108.074502)]

* [Persistent homology as a probe for center vortices and deconfinement in SU(2) lattice gauge theory](https://arxiv.org/abs/2211.16273) [[DOI](https://doi.org/10.22323/1.430.0387)]

* [Error reduction using machine learning on Ising worm simulation](https://arxiv.org/abs/2212.02365) [[DOI](https://doi.org/10.22323/1.430.0018)]

* [A machine learning approach to the classification of phase transitions in many flavor QCD](https://arxiv.org/abs/2211.16232) [[DOI](https://doi.org/10.22323/1.430.0027)]

* [Applications of Lattice Gauge Equivariant Neural Networks](https://arxiv.org/abs/2212.00832) [[DOI](https://doi.org/10.1051/epjconf/202227409001)]

* [Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions](https://arxiv.org/abs/2212.06835)

* [Learning trivializing gradient flows for lattice gauge theories](https://arxiv.org/abs/2212.08469) [[DOI](https://doi.org/10.1103/PhysRevD.107.L051504)]

* [Learning trivializing gradient flows for lattice gauge theories](https://arxiv.org/abs/2212.08469) [[DOI](https://doi.org/10.1103/PhysRevD.107.L051504)]

* [Unpolarized proton PDF at NNLO from lattice QCD with physical quark masses](https://arxiv.org/abs/2212.12569) [[DOI](https://doi.org/10.1103/PhysRevD.107.074509)]

* [Schwinger mechanism for gluons from lattice QCD](https://arxiv.org/abs/2211.12594) [[DOI](https://doi.org/10.1016/j.physletb.2023.137906)]

* [Deep Learning of Fermion Sign Fluctuations](https://arxiv.org/abs/2212.14606) [[DOI](https://doi.org/10.1103/PhysRevD.107.114505)]

* [Machine learning phases of an Abelian gauge theory](https://arxiv.org/abs/2212.14655) [[DOI](https://doi.org/10.1093/ptep/ptad096)]

* [Gauge-equivariant neural networks as preconditioners in lattice QCD](https://arxiv.org/abs/2302.05419) [[DOI](https://doi.org/10.1103/PhysRevD.108.034503)]

* [Learning Trivializing Flows](https://arxiv.org/abs/2302.08408) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11838-8)]

* [Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories](https://arxiv.org/abs/2302.14082) [[DOI](https://doi.org/10.1103/PhysRevD.108.114501)]

* [Geometrical aspects of lattice gauge equivariant convolutional neural networks](https://arxiv.org/abs/2303.11448)

* [Exploring QCD matter in extreme conditions with Machine Learning](https://arxiv.org/abs/2303.15136) [[DOI](https://doi.org/10.1016/j.ppnp.2023.104084)]

* [Exotic Tetraquark states with two $\bar{b}$-quarks and $J^P](https://arxiv.org/abs/2303.17295) [[DOI](https://doi.org/10.1103/PhysRevD.107.114510)]

* [Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations](https://arxiv.org/abs/2304.01798)

* [A variational Monte Carlo algorithm for lattice gauge theories with continuous gauge groups: a study of (2+1)-dimensional compact QED with dynamical fermions at finite density](https://arxiv.org/abs/2304.05916) [[DOI](https://doi.org/10.1103/PhysRevResearch.5.043128)]

* [Evidence of the Schwinger mechanism from lattice QCD](https://arxiv.org/abs/2304.07800) [[DOI](https://doi.org/10.1007/s00601-023-01813-0)]

* [Gauge-equivariant pooling layers for preconditioners in lattice QCD](https://arxiv.org/abs/2304.10438)

* [Sampling $U(1)$ gauge theory using a re-trainable conditional flow-based model](https://arxiv.org/abs/2306.00581) [[DOI](https://doi.org/10.1103/PhysRevD.108.074518)]

* [Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness](https://arxiv.org/abs/2306.01647) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12513-2)]

* [Teaching to extract spectral densities from lattice correlators to a broad audience of learning-machines](https://arxiv.org/abs/2307.00808) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12399-0)]

* [Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows](https://arxiv.org/abs/2307.01107) [[DOI](https://doi.org/10.1007/JHEP02(2024)048)]

* [Signal-to-noise improvement through neural network contour deformations for 3D $SU(2)$ lattice gauge theory](https://arxiv.org/abs/2309.00600) [[DOI](https://doi.org/10.22323/1.453.0043)]

* [Application of the path optimization method to a discrete spin system](https://arxiv.org/abs/2309.06018) [[DOI](https://doi.org/10.1103/PhysRevD.108.094504)]

* [Breaking Free with AI: The Deconfinement Transition](https://arxiv.org/abs/2309.07225)

* [Learning Trivializing Flows in a $\phi^4$ theory from coarser lattices](https://arxiv.org/abs/2310.03381) [[DOI](https://doi.org/10.22323/1.453.0013)]

* [Lattice real-time simulations with learned optimal kernels](https://arxiv.org/abs/2310.08053) [[DOI](https://doi.org/10.1103/PhysRevD.109.L031502)]

* [Equivariant Transformer is all you need](https://arxiv.org/abs/2310.13222) [[DOI](https://doi.org/10.22323/1.453.0001)]

* [Generative Diffusion Models for Lattice Field Theory](https://arxiv.org/abs/2311.03578)

* [Study of topological quantities of lattice QCD by a modified Wasserstein generative adversarial network](https://arxiv.org/abs/2311.10108) [[DOI](https://doi.org/10.1103/PhysRevD.109.074509)]

* [Extraction of the microscopic properties of quasi-particles using deep neural networks](https://arxiv.org/abs/2311.15984)

* [Fixed point actions from convolutional neural networks](https://arxiv.org/abs/2311.17816) [[DOI](https://doi.org/10.22323/1.453.0038)]

* [A study of topological quantities of lattice QCD by a modified DCGAN frame](https://arxiv.org/abs/2312.03023) [[DOI](https://doi.org/10.1088/1674-1137/ad2b51)]

* [MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory](https://arxiv.org/abs/2312.08936)

* [Mitigating a discrete sign problem with extreme learning machines](https://arxiv.org/abs/2312.12636)

* [Flow-based sampling for lattice field theories](https://arxiv.org/abs/2401.01297)

* [Exploring the Critical Points in QCD with Multi-Point Pad\'e and Machine Learning Techniques in (2+1)-flavor QCD](https://arxiv.org/abs/2401.05651)

* [Machine learning a fixed point action for SU(3) gauge theory with a gauge equivariant convolutional neural network](https://arxiv.org/abs/2401.06481)

* [The dependence of observables on action parameters](https://arxiv.org/abs/2401.06456) [[DOI](https://doi.org/10.22323/1.453.0020)]

* [Machine learning holographic black hole from lattice QCD equation of state](https://arxiv.org/abs/2401.06417) [[DOI](https://doi.org/10.1103/PhysRevD.109.L051902)]

* [Advances in algorithms for solvers and gauge generation](https://arxiv.org/abs/2401.16620)

* [Lattice simulation of $SU(2)$ dark glueball with machine learning](https://arxiv.org/abs/2402.03959)

* [Mitigating topological freezing using out-of-equilibrium simulations](https://arxiv.org/abs/2402.06561) [[DOI](https://doi.org/10.1007/JHEP04(2024)126)]

* [Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators](https://arxiv.org/abs/2402.06607)

* [Machine learning mapping of lattice correlated data](https://arxiv.org/abs/2402.07450)

* [Fine grinding localized updates via gauge equivariant flows in the 2D Schwinger model](https://arxiv.org/abs/2402.12176) [[DOI](https://doi.org/10.22323/1.453.0022)]

* [Multiscale Normalizing Flows for Gauge Theories](https://arxiv.org/abs/2404.10819) [[DOI](https://doi.org/10.22323/1.453.0035)]

* [Flow-based Nonperturbative Simulation of First-order Phase Transitions](https://arxiv.org/abs/2404.18323)

* [Flavor dependent Critical endpoint from holographic QCD through machine learning](https://arxiv.org/abs/2405.06179)

* [Building imaginary-time thermal filed theory with artificial neural networks](https://arxiv.org/abs/2405.10493)

* [Deep learning lattice gauge theories](https://arxiv.org/abs/2405.14830)

* [Generating configurations of increasing lattice size with machine learning and the inverse renormalization group](https://arxiv.org/abs/2405.16288)

* [QCD Phase Diagram at finite Magnetic Field and Chemical Potential: A Holographic Approach Using Machine Learning](https://arxiv.org/abs/2406.12772)

* [Berezinskii--Kosterlitz--Thouless transition of the two-dimensional $XY$ model on the honeycomb lattice](https://arxiv.org/abs/2406.14812)

* [Disordered Lattice Glass $\phi^{4}$ Quantum Field Theory](https://arxiv.org/abs/2407.06569)

* [Study of the mass of pseudoscalar glueball with a deep neural network](https://arxiv.org/abs/2407.12010)

###  Function Approximation

* [Elvet -- a neural network-based differential equation and variational problem solver](https://arxiv.org/abs/2103.14575)

* [The DNNLikelihood: enhancing likelihood distribution with Deep Learning](https://arxiv.org/abs/1911.03305) [[DOI](https://doi.org/10.1140/epjc/s10052-020-8230-1)]

* [Invariant polynomials and machine learning](https://arxiv.org/abs/2104.12733) [[DOI](https://doi.org/10.17863/CAM.80156)]

* [Function Approximation for High-Energy Physics: Comparing Machine Learning and Interpolation Methods](https://arxiv.org/abs/2111.14788) [[DOI](https://doi.org/10.21468/SciPostPhys.12.6.187)]

* [Reconstructing spectral functions via automatic differentiation](https://arxiv.org/abs/2111.14760) [[DOI](https://doi.org/10.1103/PhysRevD.106.L051502)]

* [Robust and Provably Monotonic Networks](https://arxiv.org/abs/2112.00038) [[DOI](https://doi.org/10.1088/2632-2153/aced80)]

* [Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector](https://arxiv.org/abs/2211.01505)

* [Determination of the distribution of strong coupling constant with machine learning](https://arxiv.org/abs/2303.07968)

* [A Modern Global Extraction of the Sivers Function](https://arxiv.org/abs/2304.14328) [[DOI](https://doi.org/10.1103/PhysRevD.108.054007)]

* [The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows](https://arxiv.org/abs/2309.09743)

* [Calabi-Yau Links and Machine Learning](https://arxiv.org/abs/2401.11550)

###  Symbolic Regression

* [Back to the Formula -- LHC Edition](https://arxiv.org/abs/2109.10414) [[DOI](https://doi.org/10.21468/SciPostPhys.16.1.037)]

* [Discover the GellMann-Okubo formula with machine learning](https://arxiv.org/abs/2208.03165) [[DOI](https://doi.org/10.1088/0256-307X/39/11/111201)]

* [Rediscovery of Numerical Luscher's Formula from the Neural Network](https://arxiv.org/abs/2210.02184) [[DOI](https://doi.org/10.1088/1674-1137/ad3b9c)]

* [Research on the distribution formula of QCD strong coupling constant in medium and high energy scale region based on symbolic regression algorithm](https://arxiv.org/abs/2304.07682) [[DOI](https://doi.org/10.1088/0256-307X/41/3/031201)]

* [Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS](https://arxiv.org/abs/2404.10971)

###  Monitoring

* [First demonstration of neural sensing and control in a kilometer-scale gravitational wave observatory](https://arxiv.org/abs/2301.06221) [[DOI](https://doi.org/10.1103/PhysRevApplied.20.064041)]

* [High-availability displacement sensing with multi-channel self mixing interferometry](https://arxiv.org/abs/2302.00065) [[DOI](https://doi.org/10.1364/OE.485955)]

* [Machine Learning based tool for CMS RPC currents quality monitoring](https://arxiv.org/abs/2302.02764) [[DOI](https://doi.org/10.1016/j.nima.2023.168449)]

* [Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC](https://arxiv.org/abs/2303.15291)

* [Magnetic field regression using artificial neural networks for cold atom experiments](https://arxiv.org/abs/2305.18822) [[DOI](https://doi.org/10.1088/1674-1056/ad0cc8)]

* [Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter](https://arxiv.org/abs/2308.16659)

* [How to Understand Limitations of Generative Networks](https://arxiv.org/abs/2305.16774) [[DOI](https://doi.org/10.21468/SciPostPhys.16.1.031)]

* [Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter](https://arxiv.org/abs/2309.10157)

* [GAMPix: a novel fine-grained, low-noise and ultra-low power pixelated charge readout for TPCs](https://arxiv.org/abs/2402.00902)

* [Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS](https://arxiv.org/abs/2404.10971)

##  Equivariant networks.

* [Equivariant flow-based sampling for lattice gauge theory](https://arxiv.org/abs/2003.06413) [[DOI](https://doi.org/10.1103/PhysRevLett.125.121601)]

* [Equivariant Energy Flow Networks for Jet Tagging](https://arxiv.org/abs/2012.00964) [[DOI](https://doi.org/10.1103/PhysRevD.103.074022)]

* [Lattice gauge equivariant convolutional neural networks](https://arxiv.org/abs/2012.12901) [[DOI](https://doi.org/10.1103/PhysRevLett.128.032003)]

* [Equivariance and generalization in neural networks](https://arxiv.org/abs/2112.12493) [[DOI](https://doi.org/10.1051/epjconf/202225809001)]

* [An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging](https://arxiv.org/abs/2201.08187) [[DOI](https://doi.org/10.1007/JHEP07(2022)030)]

* [Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help](https://arxiv.org/abs/2201.02564) [[DOI](https://doi.org/10.1016/j.cpc.2022.108547)]

* [Symmetry Group Equivariant Architectures for Physics](https://arxiv.org/abs/2203.06153)

* [Applications of Lattice Gauge Equivariant Neural Networks](https://arxiv.org/abs/2212.00832) [[DOI](https://doi.org/10.1051/epjconf/202227409001)]

* [PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics](https://arxiv.org/abs/2211.00454)

* [Lorentz group equivariant autoencoders](https://arxiv.org/abs/2212.07347) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11633-5)]

* [Gauge-equivariant neural networks as preconditioners in lattice QCD](https://arxiv.org/abs/2302.05419) [[DOI](https://doi.org/10.1103/PhysRevD.108.034503)]

* [Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles](https://arxiv.org/abs/2301.05638) [[DOI](https://doi.org/10.1088/2632-2153/acd989)]

* [Geometrical aspects of lattice gauge equivariant convolutional neural networks](https://arxiv.org/abs/2303.11448)

* [EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets](https://arxiv.org/abs/2301.08128) [[DOI](https://doi.org/10.21468/SciPostPhys.15.4.130)]

* [Discovering Sparse Representations of Lie Groups with Machine Learning](https://arxiv.org/abs/2302.05383) [[DOI](https://doi.org/10.1016/j.physletb.2023.138086)]

* [Gauge-equivariant pooling layers for preconditioners in lattice QCD](https://arxiv.org/abs/2304.10438)

* [Equivariant Graph Neural Networks for Charged Particle Tracking](https://arxiv.org/abs/2304.05293)

* [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)]

* [Safe but Incalculable: Energy-weighting is not all you need](https://arxiv.org/abs/2311.07652)

* [Neural ODEs for holographic transport models without translation symmetry](https://arxiv.org/abs/2401.09946)

* [Learning New Physics from Data -- a Symmetrized Approach](https://arxiv.org/abs/2401.09530)

* [Rotation-equivariant graph neural network for learning hadronic SMEFT effects](https://arxiv.org/abs/2401.10323) [[DOI](https://doi.org/10.1103/PhysRevD.109.076012)]

* [Equivariant, Safe and Sensitive -- Graph Networks for New Physics](https://arxiv.org/abs/2402.12449)

* [CapsLorentzNet: Integrating Physics Inspired Features with Graph Convolution](https://arxiv.org/abs/2403.11826)

* [Foundations of automatic feature extraction at LHC--point clouds and graphs](https://arxiv.org/abs/2404.16207)

* [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806)

* [Equivariant neural networks for robust $\textit{CP}$ observables](https://arxiv.org/abs/2405.13524)

* [Learning Group Invariant Calabi-Yau Metrics by Fundamental Domain Projections](https://arxiv.org/abs/2407.06914)

##  Decorrelation methods.

* [Learning to Pivot with Adversarial Networks](https://arxiv.org/abs/1611.01046) [[url](https://papers.nips.cc/paper/2017/hash/48ab2f9b45957ab574cf005eb8a76760-Abstract.html)]

* [Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure](https://arxiv.org/abs/1603.00027) [[DOI](https://doi.org/10.1007/JHEP05(2016)156)]

* Convolved Substructure: Analytically Decorrelating Jet Substructure Observables

* [uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers](https://arxiv.org/abs/1305.7248) [[DOI](https://doi.org/10.1088/1748-0221/8/12/P12013)]

* [Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://arxiv.org/abs/1703.03507) [[DOI](https://doi.org/10.1103/PhysRevD.96.074034)]

* [Mass Agnostic Jet Taggers](https://arxiv.org/abs/1908.08959) [[DOI](https://doi.org/10.21468/SciPostPhys.8.1.011)]

* [Performance of mass-decorrelated jet substructure](http://cds.cern.ch/record/2630973)

* [DisCo Fever: Robust Networks Through Distance Correlation](https://arxiv.org/abs/2001.05310) [[DOI](https://doi.org/10.1103/PhysRevLett.125.122001)]

* [QBDT, a new boosting decision tree method with systematical uncertainties into training for High Energy Physics](https://arxiv.org/abs/1810.08387) [[DOI](https://doi.org/10.1016/j.nima.2019.03.088)]

* [Machine Learning Uncertainties with Adversarial Neural Networks](https://arxiv.org/abs/1807.08763) [[DOI](https://doi.org/10.1140/epjc/s10052-018-6511-8)]

* [Reducing the dependence of the neural network function to systematic uncertainties in the input space](https://arxiv.org/abs/1907.11674) [[DOI](https://doi.org/10.1007/s41781-020-00037-9)]

* [New approaches for boosting to uniformity](https://arxiv.org/abs/1410.4140) [[DOI](https://doi.org/10.1088/1748-0221/10/03/T03002)]

* [A deep neural network to search for new long-lived particles decaying to jets](https://arxiv.org/abs/1912.12238) [[DOI](https://doi.org/10.1088/2632-2153/ab9023)]

* [Adversarial domain adaptation to reduce sample bias of a high energy physics classifier](https://arxiv.org/abs/2005.00568) [[DOI](https://doi.org/10.1088/2632-2153/ac3dde)]

* [ABCDisCo: Automating the ABCD Method with Machine Learning](https://arxiv.org/abs/2007.14400) [[DOI](https://doi.org/10.1103/PhysRevD.103.035021)]

* [Enhancing searches for resonances with machine learning and moment decomposition](https://arxiv.org/abs/2010.09745) [[DOI](https://doi.org/10.1007/JHEP04(2021)070)]

* [A Cautionary Tale of Decorrelating Theory Uncertainties](https://arxiv.org/abs/2109.08159) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10012-w)]

* [Metalearning and data augmentation for mass-generalized jet taggers](https://arxiv.org/abs/2111.06047) [[DOI](https://doi.org/10.1103/PhysRevD.105.094030)]

* [Online-compatible Unsupervised Non-resonant Anomaly Detection](https://arxiv.org/abs/2111.06417) [[DOI](https://doi.org/10.1103/PhysRevD.105.055006)]

* [Decorrelation with conditional normalizing flows](https://arxiv.org/abs/2211.02486)

* [Feature Selection with Distance Correlation](https://arxiv.org/abs/2212.00046) [[DOI](https://doi.org/10.1103/PhysRevD.109.054009)]

* [Partial wave analysis of $\tau^-\to\pi^-\pi^+\pi^-\nu_\tau$ at Belle](https://arxiv.org/abs/2211.11696) [[DOI](https://doi.org/10.22323/1.414.1034)]

* [Decorrelation using Optimal Transport](https://arxiv.org/abs/2307.05187)

##  Generative models / density estimation

###  GANs

* [Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis](https://arxiv.org/abs/1701.05927) [[DOI](https://doi.org/10.1007/s41781-017-0004-6)]

* [Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters](https://arxiv.org/abs/1705.02355) [[DOI](https://doi.org/10.1103/PhysRevLett.120.042003)]

* [CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks](https://arxiv.org/abs/1712.10321) [[DOI](https://doi.org/10.1103/PhysRevD.97.014021)]

* [Image-based model parameter optimization using Model-Assisted Generative Adversarial Networks](https://arxiv.org/abs/1812.00879) [[DOI](https://doi.org/10.1109/TNNLS.2020.2969327)]

* [How to GAN Event Subtraction](https://arxiv.org/abs/1912.08824) [[DOI](https://doi.org/10.21468/SciPostPhysCore.3.2.009)]

* [Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description](https://arxiv.org/abs/1912.02748) [[DOI](https://doi.org/10.1088/1742-6596/1525/1/012081)]

* [How to GAN away Detector Effects](https://arxiv.org/abs/1912.00477) [[DOI](https://doi.org/10.21468/SciPostPhys.8.4.070)]

* [3D convolutional GAN for fast simulation](https://doi.org/10.1051/epjconf/201921402010)

* [Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks](https://arxiv.org/abs/1909.04451) [[DOI](https://doi.org/10.1088/1748-0221/14/11/P11028)]

* [Lund jet images from generative and cycle-consistent adversarial networks](https://arxiv.org/abs/1909.01359) [[DOI](https://doi.org/10.1140/epjc/s10052-019-7501-1)]

* [How to GAN LHC Events](https://arxiv.org/abs/1907.03764) [[DOI](https://doi.org/10.21468/SciPostPhys.7.6.075)]

* [Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model](https://arxiv.org/abs/1903.02556) [[DOI](https://doi.org/10.1007/JHEP05(2019)181)]

* [DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC](https://arxiv.org/abs/1903.02433) [[DOI](https://doi.org/10.1007/JHEP08(2019)110)]

* [LHC analysis-specific datasets with Generative Adversarial Networks](https://arxiv.org/abs/1901.05282)

* [Generative Models for Fast Calorimeter Simulation.LHCb case](https://arxiv.org/abs/1812.01319) [[DOI](https://doi.org/10.1051/epjconf/201921402034)]

* [Deep generative models for fast shower simulation in ATLAS](http://cds.cern.ch/record/2630433)

* [Regressive and generative neural networks for scalar field theory](https://arxiv.org/abs/1810.12879) [[DOI](https://doi.org/10.1103/PhysRevD.100.011501)]

* [Three dimensional Generative Adversarial Networks for fast simulation](https://doi.org/10.1088/1742-6596/1085/3/032016)

* [Generative models for fast simulation](https://doi.org/10.1088/1742-6596/1085/2/022005)

* [Unfolding with Generative Adversarial Networks](https://arxiv.org/abs/1806.00433)

* [Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks](https://arxiv.org/abs/1805.00850) [[DOI](https://doi.org/10.1007/s41781-018-0015-y)]

* [Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks](https://arxiv.org/abs/1802.03325) [[DOI](https://doi.org/10.1007/s41781-018-0008-x)]

* [Generative models for fast cluster simulations in the TPC for the ALICE experiment](https://doi.org/10.1051/epjconf/201921406003)

* [RICH 2018](https://arxiv.org/abs/1903.11788) [[DOI](https://doi.org/10.1016/j.nima.2019.01.031)]

* [GANs for generating EFT models](https://arxiv.org/abs/1809.02612) [[DOI](https://doi.org/10.1016/j.physletb.2020.135798)]

* [Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network](https://arxiv.org/abs/1807.01954) [[DOI](https://doi.org/10.1007/s41781-018-0019-7)]

* [Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks](https://arxiv.org/abs/1811.03533) [[DOI](https://doi.org/10.1088/2632-2153/abae73)]

* [Tips and Tricks for Training GANs with Physics Constraints](https://dl4physicalsciences.github.io/files/nips_dlps_2017_26.pdf)

* [Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters](https://arxiv.org/abs/1711.08813) [[DOI](https://doi.org/10.1088/1742-6596/1085/4/042017)]

* [Next Generation Generative Neural Networks for HEP](https://doi.org/10.1051/epjconf/201921409005)

* [Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf)

* [Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics](https://arxiv.org/abs/1912.06794) [[DOI](https://doi.org/10.1140/epjc/s10052-020-8251-9)]

* [A Novel Scenario in the Semi-constrained NMSSM](https://arxiv.org/abs/2002.05554) [[DOI](https://doi.org/10.1007/JHEP06(2020)078)]

* [Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed](https://arxiv.org/abs/2005.05334) [[DOI](https://doi.org/10.1007/s41781-021-00056-0)]

* [AI-based Monte Carlo event generator for electron-proton scattering](https://arxiv.org/abs/2008.03151) [[DOI](https://doi.org/10.1103/PhysRevD.106.096002)]

* [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/{10.1088/1748-0221/15/11/p11004)]

* [GANplifying Event Samples](https://arxiv.org/abs/2008.06545) [[DOI](https://doi.org/10.21468/SciPostPhys.10.6.139)]

* [Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics](https://arxiv.org/abs/2012.00173)

* [Simulating the time projection chamber responses at the MPD detector using generative adversarial networks](https://arxiv.org/abs/2012.04595) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09366-4)]

* [Explainable machine learning of the underlying physics of high-energy particle collisions](https://arxiv.org/abs/2012.06582) [[DOI](https://doi.org/10.1016/j.physletb.2022.137055)]

* [A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network](https://arxiv.org/abs/2102.11524) [[DOI](https://doi.org/10.1007/s40042-021-00095-1)]

* [Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case](https://arxiv.org/abs/2103.10142) [[DOI](https://doi.org/10.5220/0010245002510258)]

* [Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations](https://arxiv.org/abs/2103.13698)

* [Compressing PDF sets using generative adversarial networks](https://arxiv.org/abs/2104.04535) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09338-8)]

* [Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations](https://arxiv.org/abs/2105.08960) [[DOI](https://doi.org/10.1051/epjconf/202125103042)]

* [The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHC](https://arxiv.org/abs/2105.14933)

* [Latent Space Refinement for Deep Generative Models](https://arxiv.org/abs/2106.00792)

* [Particle Cloud Generation with Message Passing Generative Adversarial Networks](https://arxiv.org/abs/2106.11535)

* [Black-Box Optimization with Local Generative Surrogates](https://arxiv.org/abs/2002.04632) [[url](https://proceedings.neurips.cc/paper/2020/hash/a878dbebc902328b41dbf02aa87abb58-Abstract.html)]

* [Fast Simulation of a High Granularity Calorimeter by Generative Adversarial Networks](https://arxiv.org/abs/2109.07388) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10258-4)]

* [Photon detection probability prediction using one-dimensional generative neural network](https://arxiv.org/abs/2109.07277) [[DOI](https://doi.org/10.1088/2632-2153/ac58e2)]

* [Polarization measurement for the dileptonic channel of $W^+ W^-$ scattering using generative adversarial network](https://arxiv.org/abs/2109.09924) [[DOI](https://doi.org/10.1103/PhysRevD.105.016005)]

* [Style-based quantum generative adversarial networks for Monte Carlo events](https://arxiv.org/abs/2110.06933) [[DOI](https://doi.org/10.22331/q-2022-08-17-777)]

* [Machine Learning for the LHCb Simulation](https://arxiv.org/abs/2110.07925)

* [Non-Parametric Data-Driven Background Modelling using Conditional Probabilities](https://arxiv.org/abs/2112.00650) [[DOI](https://doi.org/10.1007/JHEP10(2022)001)]

* [SymmetryGAN: Symmetry Discovery with Deep Learning](https://arxiv.org/abs/2112.05722) [[DOI](https://doi.org/10.1103/PhysRevD.105.096031)]

* [Hadrons, Better, Faster, Stronger](https://arxiv.org/abs/2112.09709) [[DOI](https://doi.org/10.1088/2632-2153/ac7848)]

* [Calomplification - The Power of Generative Calorimeter Models](https://arxiv.org/abs/2202.07352) [[DOI](https://doi.org/10.1088/1748-0221/17/09/P09028)]

* [Towards a Deep Learning Model for Hadronization](https://arxiv.org/abs/2203.12660) [[DOI](https://doi.org/10.1103/PhysRevD.106.096020)]

* [Towards Reliable Neural Generative Modeling of Detectors](https://arxiv.org/abs/2204.09947) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012130)]

* [Generative Surrogates for Fast Simulation: TPC Case](https://arxiv.org/abs/2207.04340) [[DOI](https://doi.org/10.1016/j.nima.2022.167743)]

* [GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response](https://arxiv.org/abs/2207.06329) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012086)]

* [Deep generative models for fast photon shower simulation in ATLAS](https://arxiv.org/abs/2210.06204) [[DOI](https://doi.org/10.1007/s41781-023-00106-9)]

* [Generative models uncertainty estimation](https://arxiv.org/abs/2210.09767) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012088)]

* [EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets](https://arxiv.org/abs/2301.08128) [[DOI](https://doi.org/10.21468/SciPostPhys.15.4.130)]

* [Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography](https://arxiv.org/abs/2301.11865) [[DOI](https://doi.org/10.22323/1.420.0041)]

* [Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning](https://arxiv.org/abs/2303.08046)

* [Generative adversarial networks for scintillation signal simulation in EXO-200](https://arxiv.org/abs/2303.06311) [[DOI](https://doi.org/10.1088/1748-0221/18/06/P06005)]

* [New Angles on Fast Calorimeter Shower Simulation](https://arxiv.org/abs/2303.18150) [[DOI](https://doi.org/10.1088/2632-2153/acefa9)]

* [Fitting a Deep Generative Hadronization Model](https://arxiv.org/abs/2305.17169) [[DOI](https://doi.org/10.1007/JHEP09(2023)084)]

* [Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN](https://arxiv.org/abs/2306.13606) [[DOI](https://doi.org/10.1063/5.0203567)]

* [Toward a generative modeling analysis of CLAS exclusive $2\pi$ photoproduction](https://arxiv.org/abs/2307.04450) [[DOI](https://doi.org/10.1103/PhysRevD.108.094030)]

* [Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss](https://arxiv.org/abs/2303.11428)

* [SR-GAN for SR-gamma: photon super resolution at collider experiments](https://arxiv.org/abs/2308.09025) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12178-3)]

* [CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation](https://arxiv.org/abs/2309.06515)

* [DeepTreeGAN: Fast Generation of High Dimensional Point Clouds](https://arxiv.org/abs/2311.12616) [[DOI](https://doi.org/10.1051/epjconf/202429509010)]

* [DeepTreeGANv2: Iterative Pooling of Point Clouds](https://arxiv.org/abs/2312.00042)

* [Integrating Particle Flavor into Deep Learning Models for Hadronization](https://arxiv.org/abs/2312.08453)

* [cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation](https://arxiv.org/abs/2401.16356)

* [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704)

###  (Variational) Autoencoders

* [Deep Learning as a Parton Shower](https://arxiv.org/abs/1807.03685) [[DOI](https://doi.org/10.1007/JHEP12(2018)021)]

* [Deep generative models for fast shower simulation in ATLAS](http://cds.cern.ch/record/2630433)

* [Variational Autoencoders for Anomalous Jet Tagging](https://arxiv.org/abs/2007.01850) [[DOI](https://doi.org/10.1103/PhysRevD.107.016002)]

* [Variational Autoencoders for Jet Simulation](https://arxiv.org/abs/2009.04842)

* [Foundations of a Fast, Data-Driven, Machine-Learned Simulator](https://arxiv.org/abs/2101.08944) [[DOI](https://doi.org/10.1038/s41598-022-10966-7)]

* [Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network](https://arxiv.org/abs/2102.12491) [[DOI](https://doi.org/10.1051/epjconf/202125103003)]

* [Bump Hunting in Latent Space](https://arxiv.org/abs/2103.06595) [[DOI](https://doi.org/10.1103/PhysRevD.105.115009)]

* [{End-to-end Sinkhorn Autoencoder with Noise Generator](https://arxiv.org/abs/2006.06704) [[DOI](https://doi.org/10.1109/ACCESS.2020.3048622)]

* [Graph Generative Models for Fast Detector Simulations in High Energy Physics](https://arxiv.org/abs/2104.01725)

* [DeepRICH: Learning Deeply Cherenkov Detectors](https://arxiv.org/abs/1911.11717) [[DOI](https://doi.org/10.1088/2632-2153/ab845a)]

* [An Exploration of Learnt Representations of W Jets](https://arxiv.org/abs/2109.10919)

* [Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC](https://arxiv.org/abs/2109.15197)

* [Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows](https://arxiv.org/abs/2110.08508) [[DOI](https://doi.org/10.3389/fdata.2022.803685)]

* [Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance](https://arxiv.org/abs/2111.12849)

* [Hadrons, Better, Faster, Stronger](https://arxiv.org/abs/2112.09709) [[DOI](https://doi.org/10.1088/2632-2153/ac7848)]

* [Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders](https://arxiv.org/abs/2203.00520) [[DOI](https://doi.org/10.1088/2632-2153/ac7c56)]

* [Modeling hadronization using machine learning](https://arxiv.org/abs/2203.04983) [[DOI](https://doi.org/10.21468/SciPostPhys.14.3.027)]

* [Machine-Learning Compression for Particle Physics Discoveries](https://arxiv.org/abs/2210.11489)

* [CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation](https://arxiv.org/abs/2210.07430)

* [CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds](https://arxiv.org/abs/2211.15380)

* [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836) [[DOI](https://doi.org/10.1038/s41467-024-47704-8)]

* [Triggering Dark Showers with Conditional Dual Auto-Encoders](https://arxiv.org/abs/2306.12955)

* [Generative deep-learning reveals collective variables of Fermionic systems](https://arxiv.org/abs/2306.08348)

* [Boosting sensitivity to new physics with unsupervised anomaly detection in dijet resonance search](https://arxiv.org/abs/2308.02671) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05018-0)]

* [Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder](https://arxiv.org/abs/2311.16627)

* [CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models](https://arxiv.org/abs/2312.03179)

* [Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation](https://arxiv.org/abs/2403.13825)

* [Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation](https://arxiv.org/abs/2405.06605)

###  (Continuous) Normalizing flows

* [Flow-based generative models for Markov chain Monte Carlo in lattice field theory](https://arxiv.org/abs/1904.12072) [[DOI](https://doi.org/10.1103/PhysRevD.100.034515)]

* [Invertible Networks or Partons to Detector and Back Again](https://arxiv.org/abs/2006.06685) [[DOI](https://doi.org/10.21468/SciPostPhys.9.5.074)]

* [Equivariant flow-based sampling for lattice gauge theory](https://arxiv.org/abs/2003.06413) [[DOI](https://doi.org/10.1103/PhysRevLett.125.121601)]

* [Flows for simultaneous manifold learning and density estimation](https://arxiv.org/abs/2003.13913)

* [Exploring phase space with Neural Importance Sampling](https://arxiv.org/abs/2001.05478) [[DOI](https://doi.org/10.21468/SciPostPhys.8.4.069)]

* [Event Generation with Normalizing Flows](https://arxiv.org/abs/2001.10028) [[DOI](https://doi.org/10.1103/PhysRevD.101.076002)]

* [i-flow: High-Dimensional Integration and Sampling with Normalizing Flows](https://arxiv.org/abs/2001.05486) [[DOI](https://doi.org/10.1088/2632-2153/abab62)]

* [Anomaly Detection with Density Estimation](https://arxiv.org/abs/2001.04990) [[DOI](https://doi.org/10.1103/PhysRevD.101.075042)]

* [Data-driven Estimation of Background Distribution through Neural Autoregressive Flows](https://arxiv.org/abs/2008.03636)

* [SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle Physics](https://arxiv.org/abs/2009.14017) [[DOI](https://doi.org/10.1103/PhysRevD.103.036012)]

* [Measuring QCD Splittings with Invertible Networks](https://arxiv.org/abs/2012.09873) [[DOI](https://doi.org/10.21468/SciPostPhys.10.6.126)]

* [Efficient sampling of constrained high-dimensional theoretical spaces with machine learning](https://arxiv.org/abs/2103.06957) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09941-9)]

* [Latent Space Refinement for Deep Generative Models](https://arxiv.org/abs/2106.00792)

* [CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows](https://arxiv.org/abs/2106.05285) [[DOI](https://doi.org/10.1103/PhysRevD.107.113003)]

* [Flow-based sampling for multimodal distributions in lattice field theory](https://arxiv.org/abs/2107.00734)

* [Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences](https://arxiv.org/abs/2108.11481) [[DOI](https://doi.org/10.1088/2632-2153/ac4a3b)]

* [Classifying Anomalies THrough Outer Density Estimation (CATHODE)](https://arxiv.org/abs/2109.00546) [[DOI](https://doi.org/10.1103/PhysRevD.106.055006)]

* [Black-Box Optimization with Local Generative Surrogates](https://arxiv.org/abs/2002.04632) [[url](https://proceedings.neurips.cc/paper/2020/hash/a878dbebc902328b41dbf02aa87abb58-Abstract.html)]

* [Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference](https://arxiv.org/abs/2011.05836) [[url](https://proceedings.mlr.press/v130/vandegar21a.html)]

* [Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows](https://arxiv.org/abs/2110.08508) [[DOI](https://doi.org/10.3389/fdata.2022.803685)]

* [Inference of cosmic-ray source properties by conditional invertible neural networks](https://arxiv.org/abs/2110.09493) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10138-x)]

* [CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows](https://arxiv.org/abs/2110.11377) [[DOI](https://doi.org/10.1103/PhysRevD.107.113004)]

* [Generative Networks for Precision Enthusiasts](https://arxiv.org/abs/2110.13632) [[DOI](https://doi.org/10.21468/SciPostPhys.14.4.078)]

* [Targeting Multi-Loop Integrals with Neural Networks](https://arxiv.org/abs/2112.09145) [[DOI](https://doi.org/10.21468/SciPostPhys.12.4.129)]

* [Ephemeral Learning -- Augmenting Triggers with Online-Trained Normalizing Flows](https://arxiv.org/abs/2202.09375) [[DOI](https://doi.org/10.21468/SciPostPhys.13.4.087)]

* [Event Generation and Density Estimation with Surjective Normalizing Flows](https://arxiv.org/abs/2205.01697) [[DOI](https://doi.org/10.21468/SciPostPhys.13.3.047)]

* [$\nu$-Flows: conditional neutrino regression](https://arxiv.org/abs/2207.00664) [[DOI](https://doi.org/10.21468/SciPostPhys.14.6.159)]

* [Fourier-flow model generating Feynman paths](https://arxiv.org/abs/2211.03470) [[DOI](https://doi.org/10.1103/PhysRevD.107.056001)]

* [Learning trivializing flows](https://arxiv.org/abs/2211.12806) [[DOI](https://doi.org/10.22323/1.430.0001)]

* [CaloFlow for CaloChallenge Dataset 1](https://arxiv.org/abs/2210.14245) [[DOI](https://doi.org/10.21468/SciPostPhys.16.5.126)]

* [CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds](https://arxiv.org/abs/2211.15380)

* [JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows](https://arxiv.org/abs/2211.13630)

* [Point Cloud Generation using Transformer Encoders and Normalising Flows](https://arxiv.org/abs/2211.13623)

* [TopicFlow: Disentangling quark and gluon jets with normalizing flows](https://arxiv.org/abs/2211.16053) [[DOI](https://doi.org/10.1103/PhysRevD.107.114003)]

* [An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training](https://arxiv.org/abs/2212.08674) [[DOI](https://doi.org/10.21468/scipostphyscore.7.1.007)]

* [MadNIS -- Neural Multi-Channel Importance Sampling](https://arxiv.org/abs/2212.06172) [[DOI](https://doi.org/10.21468/SciPostPhys.15.4.141)]

* [Learning Trivializing Flows](https://arxiv.org/abs/2302.08408) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11838-8)]

* [Generative Invertible Quantum Neural Networks](https://arxiv.org/abs/2302.12906)

* [L2LFlows: Generating High-Fidelity 3D Calorimeter Images](https://arxiv.org/abs/2302.11594) [[DOI](https://doi.org/10.1088/1748-0221/18/10/P10017)]

* [Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories](https://arxiv.org/abs/2302.14082) [[DOI](https://doi.org/10.1103/PhysRevD.108.114501)]

* [Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations](https://arxiv.org/abs/2304.01798)

* [ELSA - Enhanced latent spaces for improved collider simulations](https://arxiv.org/abs/2305.07696) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11989-8)]

* [$\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://arxiv.org/abs/2307.02405) [[DOI](https://doi.org/10.1103/PhysRevD.109.012005)]

* [The Interplay of Machine Learning--based Resonant Anomaly Detection Methods](https://arxiv.org/abs/2307.11157) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12607-x)]

* [Generative modeling of nucleon-nucleon interactions](https://arxiv.org/abs/2306.13007)

* [Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow](https://arxiv.org/abs/2303.10148) [[DOI](https://doi.org/10.1088/1748-0221/19/02/P02003)]

* [Sampling $U(1)$ gauge theory using a re-trainable conditional flow-based model](https://arxiv.org/abs/2306.00581) [[DOI](https://doi.org/10.1103/PhysRevD.108.074518)]

* [Inductive CaloFlow](https://arxiv.org/abs/2305.11934) [[DOI](https://doi.org/10.1103/PhysRevD.109.033006)]

* [SuperCalo: Calorimeter shower super-resolution](https://arxiv.org/abs/2308.11700)

* [Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation](https://arxiv.org/abs/2309.06472) [[DOI](https://doi.org/10.1103/PhysRevD.108.096018)]

* [The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows](https://arxiv.org/abs/2309.09743)

* [Combining Resonant and Tail-based Anomaly Detection](https://arxiv.org/abs/2309.12918)

* [Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection](https://arxiv.org/abs/2309.13111) [[DOI](https://doi.org/10.1103/PhysRevD.109.034033)]

* [Chained Quantile Morphing with Normalizing Flows](https://arxiv.org/abs/2309.15912)

* [Learning Trivializing Flows in a $\phi^4$ theory from coarser lattices](https://arxiv.org/abs/2310.03381) [[DOI](https://doi.org/10.22323/1.453.0013)]

* [Simulation of Hadronic Interactions with Deep Generative Models](https://arxiv.org/abs/2310.07553) [[DOI](https://doi.org/10.1051/epjconf/202429509034)]

* [Systematic Evaluation of Generative Machine Learning Capability to Simulate Distributions of Observables at the Large Hadron Collider](https://arxiv.org/abs/2310.08994)

* [The MadNIS Reloaded](https://arxiv.org/abs/2311.01548)

* [Towards a data-driven model of hadronization using normalizing flows](https://arxiv.org/abs/2311.09296)

* [Fast Posterior Probability Sampling with Normalizing Flows and Its Applicability in Bayesian analysis in Particle Physics](https://arxiv.org/abs/2312.02045) [[DOI](https://doi.org/10.1103/PhysRevD.109.032008)]

* [Normalizing Flows for High-Dimensional Detector Simulations](https://arxiv.org/abs/2312.09290)

* [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618)

* [Flow-based sampling for lattice field theories](https://arxiv.org/abs/2401.01297)

* [Accelerating HEP simulations with Neural Importance Sampling](https://arxiv.org/abs/2401.09069) [[DOI](https://doi.org/10.1007/JHEP03(2024)083)]

* [Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows](https://arxiv.org/abs/2403.14076)

* [Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events](https://arxiv.org/abs/2403.14804)

* [CaloPointFlow II Generating Calorimeter Showers as Point Clouds](https://arxiv.org/abs/2403.15782)

* [One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch](https://arxiv.org/abs/2403.18582)

* [Multiscale Normalizing Flows for Gauge Theories](https://arxiv.org/abs/2404.10819) [[DOI](https://doi.org/10.22323/1.453.0035)]

* [Flow-based Nonperturbative Simulation of First-order Phase Transitions](https://arxiv.org/abs/2404.18323)

* [Unifying Simulation and Inference with Normalizing Flows](https://arxiv.org/abs/2404.18992)

* [CaloDREAM -- Detector Response Emulation via Attentive flow Matching](https://arxiv.org/abs/2405.09629)

* [Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows](https://arxiv.org/abs/2405.20407)

* [Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction](https://arxiv.org/abs/2406.01620)

* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074)

###  Diffusion Models

* [Score-based Generative Models for Calorimeter Shower Simulation](https://arxiv.org/abs/2206.11898) [[DOI](https://doi.org/10.1103/PhysRevD.106.092009)]

* [PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics](https://arxiv.org/abs/2303.05376) [[DOI](https://doi.org/10.21468/SciPostPhys.16.1.018)]

* [Fast Point Cloud Generation with Diffusion Models in High Energy Physics](https://arxiv.org/abs/2304.01266) [[DOI](https://doi.org/10.1103/PhysRevD.108.036025)]

* [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://arxiv.org/abs/2305.10399)

* [CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation](https://arxiv.org/abs/2305.04847) [[DOI](https://doi.org/10.1088/1748-0221/18/11/P11025)]

* [Jet Diffusion versus JetGPT -- Modern Networks for the LHC](https://arxiv.org/abs/2305.10475)

* [High-dimensional and Permutation Invariant Anomaly Detection](https://arxiv.org/abs/2306.03933) [[DOI](https://doi.org/10.21468/SciPostPhys.16.3.062)]

* [Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation](https://arxiv.org/abs/2307.04780) [[DOI](https://doi.org/10.1088/1748-0221/19/05/P05003)]

* [PC-Droid: Faster diffusion and improved quality for particle cloud generation](https://arxiv.org/abs/2307.06836) [[DOI](https://doi.org/10.1103/PhysRevD.109.012010)]

* [Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images](https://arxiv.org/abs/2307.13687) [[DOI](https://doi.org/10.1103/PhysRevD.109.072011)]

* [CaloDiffusion with GLaM for High Fidelity Calorimeter Simulation](https://arxiv.org/abs/2308.03876) [[DOI](https://doi.org/10.1103/PhysRevD.108.072014)]

* [Refining Fast Calorimeter Simulations with a Schr\"odinger Bridge](https://arxiv.org/abs/2308.12339)

* [Renormalizing Diffusion Models](https://arxiv.org/abs/2308.12355)

* [Improving Generative Model-based Unfolding with Schr\"odinger Bridges](https://arxiv.org/abs/2308.12351) [[DOI](https://doi.org/10.1103/PhysRevD.109.076011)]

* [CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models](https://arxiv.org/abs/2308.03847) [[DOI](https://doi.org/10.1088/1748-0221/19/02/P02001)]

* [Accelerating Markov Chain Monte Carlo sampling with diffusion models](https://arxiv.org/abs/2309.01454) [[DOI](https://doi.org/10.1016/j.cpc.2023.109059)]

* [CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation](https://arxiv.org/abs/2309.05704) [[DOI](https://doi.org/10.1088/1748-0221/19/04/P04020)]

* [EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion](https://arxiv.org/abs/2310.00049)

* [Full Phase Space Resonant Anomaly Detection](https://arxiv.org/abs/2310.06897) [[DOI](https://doi.org/10.1103/PhysRevD.109.055015)]

* [Diffusion model approach to simulating electron-proton scattering events](https://arxiv.org/abs/2310.16308)

* [The MadNIS Reloaded](https://arxiv.org/abs/2311.01548)

* [Generative Diffusion Models for Lattice Field Theory](https://arxiv.org/abs/2311.03578)

* [Kicking it Off(-shell) with Direct Diffusion](https://arxiv.org/abs/2311.17175)

* [Improving new physics searches with diffusion models for event observables and jet constituents](https://arxiv.org/abs/2312.10130) [[DOI](https://doi.org/10.1007/JHEP04(2024)109)]

* [Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation](https://arxiv.org/abs/2401.13162)

* [CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry](https://arxiv.org/abs/2402.11575)

* [End-to-end simulation of particle physics events with Flow Matching and generator Oversampling](https://arxiv.org/abs/2402.13684)

* [BUFF: Boosted Decision Tree based Ultra-Fast Flow matching](https://arxiv.org/abs/2404.18219)

* [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106)

* [CaloDREAM -- Detector Response Emulation via Attentive flow Matching](https://arxiv.org/abs/2405.09629)

* [Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN](https://arxiv.org/abs/2406.03233)

* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074)

* [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704)

###  Transformer Models

* [Learning the language of QCD jets with transformers](https://arxiv.org/abs/2303.07364) [[DOI](https://doi.org/10.1007/JHEP06(2023)184)]

* [Jet Diffusion versus JetGPT -- Modern Networks for the LHC](https://arxiv.org/abs/2305.10475)

* [$\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows](https://arxiv.org/abs/2307.02405) [[DOI](https://doi.org/10.1103/PhysRevD.109.012005)]

* [Equivariant Transformer is all you need](https://arxiv.org/abs/2310.13222) [[DOI](https://doi.org/10.22323/1.453.0001)]

* [Induced Generative Adversarial Particle Transformers](https://arxiv.org/abs/2312.04757)

* [Folded context condensation in Path Integral formalism for infinite context transformers](https://arxiv.org/abs/2405.04620)

* [Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics](https://arxiv.org/abs/2405.14806)

* [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074)

###  Physics-inspired

* [JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics](https://arxiv.org/abs/1804.09720) [[DOI](https://doi.org/10.1140/epjc/s10052-019-6607-9)]

* [Binary JUNIPR: an interpretable probabilistic model for discrimination](https://arxiv.org/abs/1906.10137) [[DOI](https://doi.org/10.1103/PhysRevLett.123.182001)]

* [Exploring the Possibility of a Recovery of Physics Process Properties from a Neural Network Model](https://arxiv.org/abs/2007.13110) [[DOI](https://doi.org/10.3390/e22090994)]

* [Explainable machine learning of the underlying physics of high-energy particle collisions](https://arxiv.org/abs/2012.06582) [[DOI](https://doi.org/10.1016/j.physletb.2022.137055)]

* [Symmetry meets AI](https://arxiv.org/abs/2103.06115) [[DOI](https://doi.org/10.21468/SciPostPhys.11.1.014)]

* [Binary Discrimination Through Next-to-Leading Order](https://arxiv.org/abs/2309.14417) [[DOI](https://doi.org/10.1007/JHEP03(2024)057)]

###  Mixture Models

* [Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning](https://arxiv.org/abs/2010.01835)

* [Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples](https://arxiv.org/abs/2103.13416) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09469-y)]

* [A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme](https://arxiv.org/abs/2110.10259) [[DOI](https://doi.org/10.1088/1748-0221/17/02/P02018)]

* [Geometry-aware Autoregressive Models for Calorimeter Shower Simulations](https://arxiv.org/abs/2212.08233)

* [Mapping QGP properties in Pb--Pb and Xe--Xe collisions at the LHC](https://arxiv.org/abs/2308.16722) [[DOI](https://doi.org/10.1103/PhysRevC.108.064908)]

###  Phase space generation

* [Efficient Monte Carlo Integration Using Boosted Decision](https://arxiv.org/abs/1707.00028)

* [Exploring phase space with Neural Importance Sampling](https://arxiv.org/abs/2001.05478) [[DOI](https://doi.org/10.21468/SciPostPhys.8.4.069)]

* [Event Generation with Normalizing Flows](https://arxiv.org/abs/2001.10028) [[DOI](https://doi.org/10.1103/PhysRevD.101.076002)]

* [i-flow: High-Dimensional Integration and Sampling with Normalizing Flows](https://arxiv.org/abs/2001.05486) [[DOI](https://doi.org/10.1088/2632-2153/abab62)]

* [Neural Network-Based Approach to Phase Space Integration](https://arxiv.org/abs/1810.11509) [[DOI](https://doi.org/10.21468/SciPostPhys.9.4.053)]

* [VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms](https://arxiv.org/abs/2002.12921) [[DOI](https://doi.org/10.1016/j.cpc.2020.107376)]

* [A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties](https://arxiv.org/abs/2007.11586) [[DOI](https://doi.org/10.1103/PhysRevD.102.076004)]

* [Improved Neural Network Monte Carlo Simulation](https://arxiv.org/abs/2009.07819) [[DOI](https://doi.org/10.21468/SciPostPhys.10.1.023)]

* [Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows](https://arxiv.org/abs/2011.13445) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.038)]

* [How to GAN Event Unweighting](https://arxiv.org/abs/2012.07873) [[DOI](https://doi.org/10.21468/SciPostPhys.10.4.089)]

* [Accelerating Monte Carlo event generation -- rejection sampling using neural network event-weight estimates](https://arxiv.org/abs/2109.11964) [[DOI](https://doi.org/10.21468/SciPostPhys.12.5.164)]

* [A machine learning approach for efficient multi-dimensional integration](https://arxiv.org/abs/2009.06697) [[DOI](https://doi.org/10.1038/s41598-021-98392-z)]

* [Multi-variable Integration with a Neural Network](https://arxiv.org/abs/2211.02834) [[DOI](https://doi.org/10.1007/JHEP03(2023)221)]

* [Machine Learning Post-Minkowskian Integrals](https://arxiv.org/abs/2209.01091) [[DOI](https://doi.org/10.1007/JHEP07(2023)181)]

* [MadNIS -- Neural Multi-Channel Importance Sampling](https://arxiv.org/abs/2212.06172) [[DOI](https://doi.org/10.21468/SciPostPhys.15.4.141)]

* [Precision studies for the partonic kinematics calculation through Machine Learning](https://arxiv.org/abs/2305.11369) [[DOI](https://doi.org/10.31349/SuplRevMexFis.4.021134)]

* [Predicting the Exclusive Diffractive Electron-Ion Cross Section at small $x$ with Machine Learning in Sar$t$re](https://arxiv.org/abs/2305.15880) [[DOI](https://doi.org/10.1016/j.cpc.2023.108872)]

* [Learning Feynman integrals from differential equations with neural networks](https://arxiv.org/abs/2312.02067)

* [Accelerating HEP simulations with Neural Importance Sampling](https://arxiv.org/abs/2401.09069) [[DOI](https://doi.org/10.1007/JHEP03(2024)083)]

###  Gaussian processes

* [Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes](https://arxiv.org/abs/1709.05681)

* [Accelerating the BSM interpretation of LHC data with machine learning](https://arxiv.org/abs/1611.02704) [[DOI](https://doi.org/10.1016/j.dark.2019.100293)]

* [$\textsf{Xsec}$: the cross-section evaluation code](https://arxiv.org/abs/2006.16273) [[DOI](https://doi.org/10.1140/epjc/s10052-020-08635-y)]

* [AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case](https://arxiv.org/abs/1911.05797) [[DOI](https://doi.org/10.1088/1748-0221/15/05/P05009)]

###  Other/hybrid

* [CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds](https://arxiv.org/abs/2211.15380)

* [Conditional Generative Modelling of Reconstructed Particles at Collider Experiments](https://arxiv.org/abs/2211.06406)

* [Ad-hoc Pulse Shape Simulation using Cyclic Positional U-Net](https://arxiv.org/abs/2212.04950)

* [Evaluating generative models in high energy physics](https://arxiv.org/abs/2211.10295) [[DOI](https://doi.org/10.1103/PhysRevD.107.076017)]

* [Jet Diffusion versus JetGPT -- Modern Networks for the LHC](https://arxiv.org/abs/2305.10475)

* [Implicit Quantile Networks For Emulation in Jet Physics](https://arxiv.org/abs/2306.15053)

* [Towards accurate real-time luminescence thermometry: an automated machine learning approach](https://arxiv.org/abs/2307.05497)

* [ML-Based Top Taggers: Performance, Uncertainty and Impact of Tower \& Tracker Data Integration](https://arxiv.org/abs/2309.01568)

##  Anomaly detection.

* [Learning New Physics from a Machine](https://arxiv.org/abs/1806.02350) [[DOI](https://doi.org/10.1103/PhysRevD.99.015014)]

* [Anomaly Detection for Resonant New Physics with Machine Learning](https://arxiv.org/abs/1805.02664) [[DOI](https://doi.org/10.1103/PhysRevLett.121.241803)]

* [Extending the search for new resonances with machine learning](https://arxiv.org/abs/1902.02634) [[DOI](https://doi.org/10.1103/PhysRevD.99.014038)]

* [Learning Multivariate New Physics](https://arxiv.org/abs/1912.12155) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08853-y)]

* [Searching for New Physics with Deep Autoencoders](https://arxiv.org/abs/1808.08992) [[DOI](https://doi.org/10.1103/PhysRevD.101.075021)]

* [QCD or What?](https://arxiv.org/abs/1808.08979) [[DOI](https://doi.org/10.21468/SciPostPhys.6.3.030)]

* [A robust anomaly finder based on autoencoder](https://arxiv.org/abs/1903.02032)

* [Variational Autoencoders for New Physics Mining at the Large Hadron Collider](https://arxiv.org/abs/1811.10276) [[DOI](https://doi.org/10.1007/JHEP05(2019)036)]

* [Adversarially-trained autoencoders for robust unsupervised new physics searches](https://arxiv.org/abs/1905.10384) [[DOI](https://doi.org/10.1007/JHEP10(2019)047)]

* [Novelty Detection Meets Collider Physics](https://arxiv.org/abs/1807.10261) [[DOI](https://doi.org/10.1103/PhysRevD.101.076015)]

* [Guiding New Physics Searches with Unsupervised Learning](https://arxiv.org/abs/1807.06038) [[DOI](https://doi.org/10.1140/epjc/s10052-019-6787-3)]

* [Does SUSY have friends? A new approach for LHC event analysis](https://arxiv.org/abs/1912.10625) [[DOI](https://doi.org/10.1007/JHEP02(2021)160)]

* [Nonparametric semisupervised classification for signal detection in high energy physics](https://arxiv.org/abs/1809.02977)

* [Uncovering latent jet substructure](https://arxiv.org/abs/1904.04200) [[DOI](https://doi.org/10.1103/PhysRevD.100.056002)]

* [Simulation Assisted Likelihood-free Anomaly Detection](https://arxiv.org/abs/2001.05001) [[DOI](https://doi.org/10.1103/PhysRevD.101.095004)]

* [Anomaly Detection with Density Estimation](https://arxiv.org/abs/2001.04990) [[DOI](https://doi.org/10.1103/PhysRevD.101.075042)]

* [A generic anti-QCD jet tagger](https://arxiv.org/abs/1709.01087) [[DOI](https://doi.org/10.1007/JHEP11(2017)163)]

* [Transferability of Deep Learning Models in Searches for New Physics at Colliders](https://arxiv.org/abs/1912.04220) [[DOI](https://doi.org/10.1103/PhysRevD.101.035042)]

* [Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders](https://arxiv.org/abs/2004.09360) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08891-6)]

* [Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark](https://arxiv.org/abs/2005.01598) [[DOI](https://doi.org/10.1140/epjp/s13360-021-01109-4)]

* [Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector](https://arxiv.org/abs/2005.02983) [[DOI](https://doi.org/10.1103/PhysRevLett.125.131801)]

* [Learning the latent structure of collider events](https://arxiv.org/abs/2005.12319) [[DOI](https://doi.org/10.1007/JHEP10(2020)206)]

* [Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders](https://arxiv.org/abs/2006.05432) [[DOI](https://doi.org/10.1140/epjc/s10052-020-08807-w)]

* [Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data](https://arxiv.org/abs/2002.12376) [[DOI](https://doi.org/10.1007/JHEP01(2021)153)]

* [Variational Autoencoders for Anomalous Jet Tagging](https://arxiv.org/abs/2007.01850) [[DOI](https://doi.org/10.1103/PhysRevD.107.016002)]

* [Anomaly Awareness](https://arxiv.org/abs/2007.14462) [[DOI](https://doi.org/10.21468/SciPostPhys.15.2.053)]

* [Unsupervised Outlier Detection in Heavy-Ion Collisions](https://arxiv.org/abs/2007.15830) [[DOI](https://doi.org/10.1088/1402-4896/abf214)]

* [Decoding Dark Matter Substructure without Supervision](https://arxiv.org/abs/2008.12731)

* [Mass Unspecific Supervised Tagging (MUST) for boosted jets](https://arxiv.org/abs/2008.12792) [[DOI](https://doi.org/10.1007/JHEP03(2021)012)]

* [Simulation-Assisted Decorrelation for Resonant Anomaly Detection](https://arxiv.org/abs/2009.02205) [[DOI](https://doi.org/10.1103/PhysRevD.104.035003)]

* [Anomaly Detection With Conditional Variational Autoencoders](https://arxiv.org/abs/2010.05531)

* [Unsupervised clustering for collider physics](https://arxiv.org/abs/2010.07106) [[DOI](https://doi.org/10.1103/PhysRevD.103.092007)]

* [Combining outlier analysis algorithms to identify new physics at the LHC](https://arxiv.org/abs/2010.07940) [[DOI](https://doi.org/10.1007/JHEP09(2021)024)]

* [Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge](https://arxiv.org/abs/2011.03550) [[DOI](https://doi.org/10.1007/JHEP06(2021)030)]

* [Uncovering hidden patterns in collider events with Bayesian probabilistic models](https://arxiv.org/abs/2012.08579) [[DOI](https://doi.org/10.22323/1.390.0238)]

* [Unsupervised in-distribution anomaly detection of new physics through conditional density estimation](https://arxiv.org/abs/2012.11638)

* [The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics](https://arxiv.org/abs/2101.08320) [[DOI](https://doi.org/10.1088/1361-6633/ac36b9)]

* [Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests](https://arxiv.org/abs/2102.07679)

* [Topological Obstructions to Autoencoding](https://arxiv.org/abs/2102.08380) [[DOI](https://doi.org/10.1007/JHEP04(2021)280)]

* [Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers](https://arxiv.org/abs/2103.03897) [[DOI](https://doi.org/10.1007/JHEP08(2021)170)]

* [Bump Hunting in Latent Space](https://arxiv.org/abs/2103.06595) [[DOI](https://doi.org/10.1103/PhysRevD.105.115009)]

* [Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection](https://arxiv.org/abs/2104.02092) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09389-x)]

* [Better Latent Spaces for Better Autoencoders](https://arxiv.org/abs/2104.08291) [[DOI](https://doi.org/10.21468/SciPostPhys.11.3.061)]

* [Autoencoders for unsupervised anomaly detection in high energy physics](https://arxiv.org/abs/2104.09051) [[DOI](https://doi.org/10.1007/JHEP06(2021)161)]

* [Via Machinae: Searching for Stellar Streams using Unsupervised Machine Learning](https://arxiv.org/abs/2104.12789) [[DOI](https://doi.org/10.1093/mnras/stab3372)]

* [Anomaly detection with Convolutional Graph Neural Networks](https://arxiv.org/abs/2105.07988) [[DOI](https://doi.org/10.1007/JHEP08(2021)080)]

* [Anomalous Jet Identification via Sequence Modeling](https://arxiv.org/abs/2105.09274) [[DOI](https://doi.org/10.1088/1748-0221/16/08/P08012)]

* [The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider](https://arxiv.org/abs/2105.14027) [[DOI](https://doi.org/10.21468/SciPostPhys.12.1.043)]

* [RanBox: Anomaly Detection in the Copula Space](https://arxiv.org/abs/2106.05747) [[DOI](https://doi.org/10.1007/JHEP01(2023)008)]

* [Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC](https://arxiv.org/abs/2106.10164) [[DOI](https://doi.org/10.21468/SciPostPhys.12.2.077)]

* [LHC physics dataset for unsupervised New Physics detection at 40 MHz](https://arxiv.org/abs/2107.02157) [[DOI](https://doi.org/10.1038/s41597-022-01187-8)]

* [New Methods and Datasets for Group Anomaly Detection From Fundamental Physics](https://arxiv.org/abs/2107.02821)

* [The Data-Directed Paradigm for BSM searches](https://arxiv.org/abs/2107.11573) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10215-1)]

* [Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider](https://arxiv.org/abs/2108.03986) [[DOI](https://doi.org/10.1038/s42256-022-00441-3)]

* [Classifying Anomalies THrough Outer Density Estimation (CATHODE)](https://arxiv.org/abs/2109.00546) [[DOI](https://doi.org/10.1103/PhysRevD.106.055006)]

* [Deep Set Auto Encoders for Anomaly Detection in Particle Physics](https://arxiv.org/abs/2109.01695) [[DOI](https://doi.org/10.21468/SciPostPhys.12.1.045)]

* [Challenges for Unsupervised Anomaly Detection in Particle Physics](https://arxiv.org/abs/2110.06948) [[DOI](https://doi.org/10.1007/JHEP03(2022)066)]

* [Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows](https://arxiv.org/abs/2110.08508) [[DOI](https://doi.org/10.3389/fdata.2022.803685)]

* [Signal-agnostic dark matter searches in direct detection data with machine learning](https://arxiv.org/abs/2110.12248) [[DOI](https://doi.org/10.1088/1475-7516/2022/02/039)]

* [Anomaly detection from mass unspecific jet tagging](https://arxiv.org/abs/2111.02647) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10058-w)]

* [A method to challenge symmetries in data with self-supervised learning](https://arxiv.org/abs/2111.05442) [[DOI](https://doi.org/10.1088/1748-0221/17/08/P08024)]

* [Stressed GANs snag desserts, a.k.a Spotting Symmetry Violation with Symmetric Functions](https://arxiv.org/abs/2111.00616)

* [Online-compatible Unsupervised Non-resonant Anomaly Detection](https://arxiv.org/abs/2111.06417) [[DOI](https://doi.org/10.1103/PhysRevD.105.055006)]

* [Event-based anomaly detection for new physics searches at the LHC using machine learning](https://arxiv.org/abs/2111.12119) [[DOI](https://doi.org/10.3390/universe8100494)]

* [Learning New Physics from an Imperfect Machine](https://arxiv.org/abs/2111.13633) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10226-y)]

* [Autoencoders for Semivisible Jet Detection](https://arxiv.org/abs/2112.02864) [[DOI](https://doi.org/10.1007/JHEP02(2022)074)]

* [Anomaly detection in high-energy physics using a quantum autoencoder](https://arxiv.org/abs/2112.04958) [[DOI](https://doi.org/10.1103/PhysRevD.105.095004)]

* [Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure](https://arxiv.org/abs/2203.01343) [[DOI](https://doi.org/10.1103/PhysRevD.106.035014)]

* [Taming modeling uncertainties with Mass Unspecific Supervised Tagging](https://arxiv.org/abs/2201.11143) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10221-3)]

* [What's Anomalous in LHC Jets?](https://arxiv.org/abs/2202.00686) [[DOI](https://doi.org/10.21468/SciPostPhys.15.4.168)]

* [Quantum Anomaly Detection for Collider Physics](https://arxiv.org/abs/2206.08391) [[DOI](https://doi.org/10.1007/JHEP02(2023)220)]

* [Detecting new physics as novelty \textemdash{} Complementarity matters](https://arxiv.org/abs/2202.02165) [[DOI](https://doi.org/10.1007/JHEP10(2022)085)]

* [Self-supervised Anomaly Detection for New Physics](https://arxiv.org/abs/2205.10380) [[DOI](https://doi.org/10.1103/PhysRevD.106.056005)]

* [Data-directed search for new physics based on symmetries of the SM](https://arxiv.org/abs/2203.07529) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10454-2)]

* [CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals](https://arxiv.org/abs/2203.09470) [[DOI](https://doi.org/10.3389/fdata.2023.899345)]

* [Learning new physics efficiently with nonparametric methods](https://arxiv.org/abs/2204.02317) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10830-y)]

* [''Flux+Mutability'': A Conditional Generative Approach to One-Class Classification and Anomaly Detection](https://arxiv.org/abs/2204.08609) [[DOI](https://doi.org/10.1088/2632-2153/ac9bcb)]

* [Boosting mono-jet searches with model-agnostic machine learning](https://arxiv.org/abs/2204.11889) [[DOI](https://doi.org/10.1007/JHEP08(2022)015)]

* [Event Generation and Density Estimation with Surjective Normalizing Flows](https://arxiv.org/abs/2205.01697) [[DOI](https://doi.org/10.21468/SciPostPhys.13.3.047)]

* [A Normalized Autoencoder for LHC Triggers](https://arxiv.org/abs/2206.14225) [[DOI](https://doi.org/10.21468/SciPostPhysCore.6.4.074)]

* [Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?](https://arxiv.org/abs/2207.07631) [[DOI](https://doi.org/10.1007/JHEP03(2023)004)]

* [Neural Embedding: Learning the Embedding of the Manifold of Physics Data](https://arxiv.org/abs/2208.05484) [[DOI](https://doi.org/10.1007/JHEP07(2023)108)]

* [Null Hypothesis Test for Anomaly Detection](https://arxiv.org/abs/2210.02226) [[DOI](https://doi.org/10.1016/j.physletb.2023.137836)]

* [Resonant anomaly detection without background sculpting](https://arxiv.org/abs/2210.14924) [[DOI](https://doi.org/10.1103/PhysRevD.107.114012)]

* [Anomaly Detection under Coordinate Transformations](https://arxiv.org/abs/2209.06225) [[DOI](https://doi.org/10.1103/PhysRevD.107.015009)]

* [Quantum-probabilistic Hamiltonian learning for generative modelling \& anomaly detection](https://arxiv.org/abs/2211.03803) [[DOI](https://doi.org/10.1103/PhysRevA.108.062422)]

* [Efficiently Moving Instead of Reweighting Collider Events with Machine Learning](https://arxiv.org/abs/2212.06155)

* [Unravelling physics beyond the standard model with classical and quantum anomaly detection](https://arxiv.org/abs/2301.10787) [[DOI](https://doi.org/10.1088/2632-2153/ad07f7)]

* [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836) [[DOI](https://doi.org/10.1038/s41467-024-47704-8)]

* [The Mass-ive Issue: Anomaly Detection in Jet Physics](https://arxiv.org/abs/2303.14134)

* [CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation](https://arxiv.org/abs/2305.04646)

* [High-dimensional and Permutation Invariant Anomaly Detection](https://arxiv.org/abs/2306.03933) [[DOI](https://doi.org/10.21468/SciPostPhys.16.3.062)]

* [The Interplay of Machine Learning--based Resonant Anomaly Detection Methods](https://arxiv.org/abs/2307.11157) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12607-x)]

* [GAN-AE : An anomaly detection algorithm for New Physics search in LHC data](https://arxiv.org/abs/2305.15179) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12169-4)]

* [Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle $X$ in hadronic final states using $\sqrt{s}](https://arxiv.org/abs/2306.03637) [[DOI](https://doi.org/10.1103/PhysRevD.108.052009)]

* [Boosting sensitivity to new physics with unsupervised anomaly detection in dijet resonance search](https://arxiv.org/abs/2308.02671) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05018-0)]

* [Autoencoder-based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter](https://arxiv.org/abs/2309.10157)

* [Combining Resonant and Tail-based Anomaly Detection](https://arxiv.org/abs/2309.12918)

* [Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection](https://arxiv.org/abs/2309.13111) [[DOI](https://doi.org/10.1103/PhysRevD.109.034033)]

* [Full Phase Space Resonant Anomaly Detection](https://arxiv.org/abs/2310.06897) [[DOI](https://doi.org/10.1103/PhysRevD.109.055015)]

* [Anomaly Detection in Presence of Irrelevant Features](https://arxiv.org/abs/2310.13057) [[DOI](https://doi.org/10.1007/JHEP02(2024)220)]

* [Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection](https://arxiv.org/abs/2311.02038) [[DOI](https://doi.org/10.1051/epjconf/202429502033)]

* [Non-resonant Anomaly Detection with Background Extrapolation](https://arxiv.org/abs/2311.12924) [[DOI](https://doi.org/10.1007/JHEP04(2024)059)]

* [Searching for gluon quartic gauge couplings at muon colliders using the auto-encoder](https://arxiv.org/abs/2311.16627)

* [Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder](https://arxiv.org/abs/2311.17162)

* [Anomaly Detection in Collider Physics via Factorized Observables](https://arxiv.org/abs/2312.00119)

* [Testing a Neural Network for Anomaly Detection in the CMS Global Trigger Test Crate during Run 3](https://arxiv.org/abs/2312.10009) [[DOI](https://doi.org/10.1088/1748-0221/19/03/C03029)]

* [Improving new physics searches with diffusion models for event observables and jet constituents](https://arxiv.org/abs/2312.10130) [[DOI](https://doi.org/10.1007/JHEP04(2024)109)]

* [Anomaly detection with flow-based fast calorimeter simulators](https://arxiv.org/abs/2312.11618)

* [Incorporating Physical Priors into Weakly-Supervised Anomaly Detection](https://arxiv.org/abs/2405.08889)

* [Accelerating Resonance Searches via Signature-Oriented Pre-training](https://arxiv.org/abs/2405.12972)

* [Anomaly-aware summary statistic from data batches](https://arxiv.org/abs/2407.01249)

* [Accelerating template generation in resonant anomaly detection searches with optimal transport](https://arxiv.org/abs/2407.19818)

* [Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring](https://arxiv.org/abs/2407.20278)

##  Foundation Models, LLMs.

* [Finetuning Foundation Models for Joint Analysis Optimization](https://arxiv.org/abs/2401.13536)

* [OmniJet-$\alpha$: The first cross-task foundation model for particle physics](https://arxiv.org/abs/2403.05618)

* [Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models](https://arxiv.org/abs/2403.07066)

* [Physics Event Classification Using Large Language Models](https://arxiv.org/abs/2404.05752)

* [Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics](https://arxiv.org/abs/2404.08001)

* [OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks](https://arxiv.org/abs/2404.16091)

##  Simulation-based (`likelihood-free') Inference

###  Parameter estimation

* [Neural Networks for Full Phase-space Reweighting and Parameter Tuning](https://arxiv.org/abs/1907.08209) [[DOI](https://doi.org/10.1103/PhysRevD.101.091901)]

* [Likelihood-free inference with an improved cross-entropy estimator](https://arxiv.org/abs/1808.00973)

* [Resonance Searches with Machine Learned Likelihood Ratios](https://arxiv.org/abs/2002.04699)

* [Constraining Effective Field Theories with Machine Learning](https://arxiv.org/abs/1805.00013) [[DOI](https://doi.org/10.1103/PhysRevLett.121.111801)]

* [A Guide to Constraining Effective Field Theories with Machine Learning](https://arxiv.org/abs/1805.00020) [[DOI](https://doi.org/10.1103/PhysRevD.98.052004)]

* [MadMiner: Machine learning-based inference for particle physics](https://arxiv.org/abs/1907.10621) [[DOI](https://doi.org/10.1007/s41781-020-0035-2)]

* [Mining gold from implicit models to improve likelihood-free inference](https://arxiv.org/abs/1805.12244) [[DOI](https://doi.org/10.1073/pnas.1915980117)]

* [Approximating Likelihood Ratios with Calibrated Discriminative  Classifiers](https://arxiv.org/abs/1506.02169)

* [Parameter Estimation using Neural Networks in the Presence of Detector Effects](https://arxiv.org/abs/2010.03569) [[DOI](https://doi.org/10.1103/PhysRevD.103.036001)]

* [Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies](https://arxiv.org/abs/2010.07032)

* [Parameter Inference from Event Ensembles and the Top-Quark Mass](https://arxiv.org/abs/2011.04666) [[DOI](https://doi.org/10.1007/JHEP09(2021)058)]

* [Measuring QCD Splittings with Invertible Networks](https://arxiv.org/abs/2012.09873) [[DOI](https://doi.org/10.21468/SciPostPhys.10.6.126)]

* [E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once](https://arxiv.org/abs/2101.07263) [[DOI](https://doi.org/10.1103/PhysRevD.103.116013)]

* [Tree boosting for learning EFT parameters](https://arxiv.org/abs/2107.10859) [[DOI](https://doi.org/10.1016/j.cpc.2022.108385)]

* [Black-Box Optimization with Local Generative Surrogates](https://arxiv.org/abs/2002.04632) [[url](https://proceedings.neurips.cc/paper/2020/hash/a878dbebc902328b41dbf02aa87abb58-Abstract.html)]

* [A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess](https://arxiv.org/abs/2110.06931) [[DOI](https://doi.org/10.1103/PhysRevD.105.063017)]

* [Machine Learning the Higgs-Top CP Phase](https://arxiv.org/abs/2110.07635) [[DOI](https://doi.org/10.1103/PhysRevD.105.035023)]

* [Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference](https://arxiv.org/abs/2110.10177) [[DOI](https://doi.org/10.1007/JHEP03(2022)017)]

* [A method for approximating optimal statistical significances with machine-learned likelihoods](https://arxiv.org/abs/2205.05952) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10944-3)]

* [New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation](https://arxiv.org/abs/2210.01680)

* [Machine-Learned Exclusion Limits without Binning](https://arxiv.org/abs/2211.04806) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12314-z)]

* [Two Invertible Networks for the Matrix Element Method](https://arxiv.org/abs/2210.00019) [[DOI](https://doi.org/10.21468/SciPostPhys.15.3.094)]

* [Deep Learning for the Matrix Element Method](https://arxiv.org/abs/2211.11910) [[DOI](https://doi.org/10.22323/1.414.0246)]

* [Learning Likelihood Ratios with Neural Network Classifiers](https://arxiv.org/abs/2305.10500) [[DOI](https://doi.org/10.1007/JHEP02(2024)136)]

* [Hierarchical Neural Simulation-Based Inference Over Event Ensembles](https://arxiv.org/abs/2306.12584)

* [Emulator-based Bayesian Inference on Non-Proportional Scintillation Models by Compton-Edge Probing](https://arxiv.org/abs/2302.05641) [[DOI](https://doi.org/10.1038/s41467-023-42574-y)]

* [Machine Learning and Kalman Filtering for Nanomechanical Mass Spectrometry](https://arxiv.org/abs/2306.00563) [[DOI](https://doi.org/10.1109/JSEN.2024.3350730)]

* [Reconstructing axion-like particles from beam dumps with simulation-based inference](https://arxiv.org/abs/2308.01353) [[DOI](https://doi.org/10.1140/epjc/s10052-024-12557-4)]

* [Simulation-based inference in the search for CP violation in leptonic WH production](https://arxiv.org/abs/2308.02882) [[DOI](https://doi.org/10.1007/JHEP04(2024)014)]

* [Scaling MadMiner with a deployment on REANA](https://arxiv.org/abs/2304.05814)

* [Precision-Machine Learning for the Matrix Element Method](https://arxiv.org/abs/2310.07752)

* [From Optimal Observables to Machine Learning: an Effective-Field-Theory Analysis of $e^+e^- \to W^+W^-$ at Future Lepton Colliders](https://arxiv.org/abs/2401.02474)

* [Rotation-equivariant graph neural network for learning hadronic SMEFT effects](https://arxiv.org/abs/2401.10323) [[DOI](https://doi.org/10.1103/PhysRevD.109.076012)]

* [Improvement and generalization of ABCD method with Bayesian inference](https://arxiv.org/abs/2402.08001)

* [Bayesian Active Search on Parameter Space: a 95 GeV Spin-0 Resonance in the ($B-L$)SSM](https://arxiv.org/abs/2404.18653)

* [Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production](https://arxiv.org/abs/2405.15847)

###  Unfolding

* [DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC](https://arxiv.org/abs/1809.06101)

* [OmniFold: A Method to Simultaneously Unfold All Observables](https://arxiv.org/abs/1911.09107) [[DOI](https://doi.org/10.1103/PhysRevLett.124.182001)]

* [Unfolding with Generative Adversarial Networks](https://arxiv.org/abs/1806.00433)

* [How to GAN away Detector Effects](https://arxiv.org/abs/1912.00477) [[DOI](https://doi.org/10.21468/SciPostPhys.8.4.070)]

* [Machine learning approach to inverse problem and unfolding procedure](https://arxiv.org/abs/1004.2006)

* [Machine learning as an instrument for data unfolding](https://arxiv.org/abs/1712.01814)

* [Advanced event reweighting using multivariate analysis](https://doi.org/10.1088/1742-6596/368/1/012028)

* [Unfolding by weighting Monte Carlo events](https://doi.org/10.1016/0168-9002(94)01067-6)

* Binning-Free Unfolding Based on Monte Carlo Migration

* [Invertible Networks or Partons to Detector and Back Again](https://arxiv.org/abs/2006.06685) [[DOI](https://doi.org/10.21468/SciPostPhys.9.5.074)]

* [Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference](https://arxiv.org/abs/2011.05836) [[url](https://proceedings.mlr.press/v130/vandegar21a.html)]

* [Foundations of a Fast, Data-Driven, Machine-Learned Simulator](https://arxiv.org/abs/2101.08944) [[DOI](https://doi.org/10.1038/s41598-022-10966-7)]

* [Comparison of Machine Learning Approach to other Unfolding Methods](https://arxiv.org/abs/2104.03036) [[DOI](https://doi.org/10.5506/APhysPolB.52.863)]

* [Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution](https://arxiv.org/abs/2105.04448)

* [Preserving New Physics while Simultaneously Unfolding All Observables](https://arxiv.org/abs/2105.09923) [[DOI](https://doi.org/10.1103/PhysRevD.104.076027)]

* [Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding](https://arxiv.org/abs/2108.12376) [[DOI](https://doi.org/10.1103/PhysRevLett.128.132002)]

* [Presenting Unbinned Differential Cross Section Results](https://arxiv.org/abs/2109.13243) [[DOI](https://doi.org/10.1088/1748-0221/17/01/P01024)]

* [Feed-forward neural network unfolding](https://arxiv.org/abs/2112.08180)

* [Optimizing Observables with Machine Learning for Better Unfolding](https://arxiv.org/abs/2203.16722) [[DOI](https://doi.org/10.1088/1748-0221/17/07/P07009)]

* [An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training](https://arxiv.org/abs/2212.08674) [[DOI](https://doi.org/10.21468/scipostphyscore.7.1.007)]

* [Unbinned Profiled Unfolding](https://arxiv.org/abs/2302.05390) [[DOI](https://doi.org/10.1103/PhysRevD.108.016002)]

* [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://arxiv.org/abs/2305.10399)

* [Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion](https://arxiv.org/abs/2404.14332)

* [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807)

* [Moment Unfolding](https://arxiv.org/abs/2407.11284)

###  Domain adaptation

* [Reweighting with Boosted Decision Trees](https://arxiv.org/abs/1608.05806) [[DOI](https://doi.org/10.1088/1742-6596/762/1/012036)]

* [Neural Networks for Full Phase-space Reweighting and Parameter Tuning](https://arxiv.org/abs/1907.08209) [[DOI](https://doi.org/10.1103/PhysRevD.101.091901)]

* [Approximating Likelihood Ratios with Calibrated Discriminative  Classifiers](https://arxiv.org/abs/1506.02169)

* [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/{10.1088/1748-0221/15/11/p11004)]

* [Neural Conditional Reweighting](https://arxiv.org/abs/2107.08979) [[DOI](https://doi.org/10.1103/PhysRevD.105.076015)]

* [Model independent measurements of Standard Model cross sections with Domain Adaptation](https://arxiv.org/abs/2207.09293) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10871-3)]

* [Mimicking non-ideal instrument behavior for hologram processing using neural style translation](https://arxiv.org/abs/2301.02757) [[DOI](https://doi.org/10.1364/OE.486741)]

* [Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting](https://arxiv.org/abs/2304.14963)

* [Peak finding algorithm for cluster counting with domain adaptation](https://arxiv.org/abs/2402.16270) [[DOI](https://doi.org/10.1016/j.cpc.2024.109208)]

* [Improving $\Lambda$ Signal Extraction with Domain Adaptation via Normalizing Flows](https://arxiv.org/abs/2403.14076)

* [Normalizing Flows for Domain Adaptation when Identifying $\Lambda$ Hyperon Events](https://arxiv.org/abs/2403.14804)

###  BSM

* [Simulation Assisted Likelihood-free Anomaly Detection](https://arxiv.org/abs/2001.05001) [[DOI](https://doi.org/10.1103/PhysRevD.101.095004)]

* [Resonance Searches with Machine Learned Likelihood Ratios](https://arxiv.org/abs/2002.04699)

* [Constraining Effective Field Theories with Machine Learning](https://arxiv.org/abs/1805.00013) [[DOI](https://doi.org/10.1103/PhysRevLett.121.111801)]

* [A Guide to Constraining Effective Field Theories with Machine Learning](https://arxiv.org/abs/1805.00020) [[DOI](https://doi.org/10.1103/PhysRevD.98.052004)]

* [Mining gold from implicit models to improve likelihood-free inference](https://arxiv.org/abs/1805.12244) [[DOI](https://doi.org/10.1073/pnas.1915980117)]

* [MadMiner: Machine learning-based inference for particle physics](https://arxiv.org/abs/1907.10621) [[DOI](https://doi.org/10.1007/s41781-020-0035-2)]

* [Use of a Generalized Energy Mover's Distance in the Search for Rare Phenomena at Colliders](https://arxiv.org/abs/2004.09360) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08891-6)]

* [Exploring Parameter Spaces with Artificial Intelligence and Machine Learning Black-Box Optimisation Algorithms](https://arxiv.org/abs/2206.09223) [[DOI](https://doi.org/10.1103/PhysRevD.107.035004)]

* [Unbinned multivariate observables for global SMEFT analyses from machine learning](https://arxiv.org/abs/2211.02058) [[DOI](https://doi.org/10.1007/JHEP03(2023)033)]

* [LHC EFT WG Report: Experimental Measurements and Observables](https://arxiv.org/abs/2211.08353)

* [On the BSM reach of four top production at the LHC](https://arxiv.org/abs/2302.08281) [[DOI](https://doi.org/10.1103/PhysRevD.108.035001)]

* [Tip of the Red Giant Branch Bounds on the Axion-Electron Coupling Revisited](https://arxiv.org/abs/2305.03113)

* [Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider](https://arxiv.org/abs/2305.01835)

* [Autoencoders for Real-Time SUEP Detection](https://arxiv.org/abs/2306.13595) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05028-y)]

* [Pinning down the leptophobic $Z^\prime$ in leptonic final states with Deep Learning](https://arxiv.org/abs/2307.01118) [[DOI](https://doi.org/10.1016/j.physletb.2023.138417)]

* [Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited](https://arxiv.org/abs/2307.13050)

* [LHC Study of Third-Generation Scalar Leptoquarks with Machine-Learned Likelihoods](https://arxiv.org/abs/2309.05407) [[DOI](https://doi.org/10.1103/PhysRevD.109.055032)]

* [Current status of the light neutralino thermal dark matter in the phenomenological MSSM](https://arxiv.org/abs/2402.07991)

* [The impact of CP-violating phases on DM observables in the cpMSSM](https://arxiv.org/abs/2402.08814)

* [Higgs couplings in SMEFT via Zh production at the HL-LHC](https://arxiv.org/abs/2403.03001)

* [Dark Matter-induced electron excitations in silicon and germanium with Deep Learning](https://arxiv.org/abs/2403.07053)

* [Probing Heavy Charged Higgs Boson Using Multivariate Technique at Gamma-Gamma Collider](https://arxiv.org/abs/2403.20293)

* [Probing intractable beyond-standard-model parameter spaces armed with Machine Learning](https://arxiv.org/abs/2404.02698)

* [Boosted four-top production at the LHC : a window to Randall-Sundrum or extended color symmetry](https://arxiv.org/abs/2404.04409)

* [Magnetic Monopole Phenomenology at Future Hadron Colliders](https://arxiv.org/abs/2404.10871)

* [Exploring Exotic Decays of the Higgs Boson to Multi-Photons at the LHC via Multimodal Learning Approaches](https://arxiv.org/abs/2405.18834)

* [Refinable modeling for unbinned SMEFT analyses](https://arxiv.org/abs/2406.19076)

###  Differentiable Simulation

* [Differentiable Matrix Elements with MadJax](https://arxiv.org/abs/2203.00057) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012137)]

* [Toward the end-to-end optimization of particle physics instruments with differentiable programming](https://arxiv.org/abs/2203.13818) [[DOI](https://doi.org/10.1016/j.revip.2023.100085)]

* [Morphing parton showers with event derivatives](https://arxiv.org/abs/2208.02274)

* [Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector](https://arxiv.org/abs/2211.01505)

* [Novel Machine Learning and Differentiable Programming Techniques applied to the VIP-2 Underground Experiment](https://arxiv.org/abs/2305.17153) [[DOI](https://doi.org/10.1088/1361-6501/ad080a)]

* [Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC](https://arxiv.org/abs/2306.04712) [[DOI](https://doi.org/10.1088/2632-2153/ad1139)]

* [Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics](https://arxiv.org/abs/2308.16680)

* [Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming](https://arxiv.org/abs/2310.05673)

* [Differentiable Vertex Fitting for Jet Flavour Tagging](https://arxiv.org/abs/2310.12804)

* [Differentiable nuclear deexcitation simulation for low energy neutrino physics](https://arxiv.org/abs/2404.00180)

##  Uncertainty Quantification

###  Interpretability

* [Jet-images — deep learning edition](https://arxiv.org/abs/1511.05190) [[DOI](https://doi.org/10.1007/JHEP07(2016)069)]

* [What is the Machine Learning?](https://arxiv.org/abs/1709.10106) [[DOI](https://doi.org/10.1103/PhysRevD.97.056009)]

* [CapsNets Continuing the Convolutional Quest](https://arxiv.org/abs/1906.11265) [[DOI](https://doi.org/10.21468/SciPostPhys.8.2.023)]

* [Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation](https://arxiv.org/abs/2011.13466) [[DOI](https://doi.org/10.1007/JHEP05(2021)208)]

* [Resurrecting $b\bar{b}h$ with kinematic shapes](https://arxiv.org/abs/2011.13945) [[DOI](https://doi.org/10.1007/JHEP04(2021)139)]

* [Safety of Quark/Gluon Jet Classification](https://arxiv.org/abs/2103.09103)

* [An Exploration of Learnt Representations of W Jets](https://arxiv.org/abs/2109.10919)

* [Explaining machine-learned particle-flow reconstruction](https://arxiv.org/abs/2111.12840)

* [Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure](https://arxiv.org/abs/2203.01343) [[DOI](https://doi.org/10.1103/PhysRevD.106.035014)]

* [Improving Parametric Neural Networks for High-Energy Physics (and Beyond)](https://arxiv.org/abs/2202.00424) [[DOI](https://doi.org/10.1088/2632-2153/ac917c)]

* [Lessons on interpretable machine learning from particle physics](https://arxiv.org/abs/2203.08021) [[DOI](https://doi.org/10.1038/s42254-022-00456-0)]

* [A Detailed Study of Interpretability of Deep Neural Network based Top Taggers](https://arxiv.org/abs/2210.04371) [[DOI](https://doi.org/10.1088/2632-2153/ace0a1)]

* [Interpretability of an Interaction Network for identifying $H \rightarrow b\bar{b}$ jets](https://arxiv.org/abs/2211.12770) [[DOI](https://doi.org/10.22323/1.414.0223)]

* [Interpretable Machine Learning Methods Applied to Jet Background Subtraction in Heavy Ion Collisions](https://arxiv.org/abs/2303.08275) [[DOI](https://doi.org/10.1103/PhysRevC.108.L021901)]

* [Interpretable deep learning models for the inference and classification of LHC data](https://arxiv.org/abs/2312.12330) [[DOI](https://doi.org/10.1007/JHEP05(2024)004)]

* [Statistical divergences in high-dimensional hypothesis testing and a modern technique for estimating them](https://arxiv.org/abs/2405.06397)

* [Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector](https://arxiv.org/abs/2406.12901)

* [Explainable AI classification for parton density theory](https://arxiv.org/abs/2407.03411)

###  Estimation

* [A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty](https://arxiv.org/abs/1909.03081) [[DOI](https://doi.org/10.21468/SciPostPhys.8.6.090)]

* [AI Safety for High Energy Physics](https://arxiv.org/abs/1910.08606)

* [Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks](https://arxiv.org/abs/1609.00607) [[DOI](https://doi.org/10.1103/PhysRevD.95.014018)]

* [Understanding Event-Generation Networks via Uncertainties](https://arxiv.org/abs/2104.04543) [[DOI](https://doi.org/10.21468/SciPostPhys.13.1.003)]

* [Exploring the Universality of Hadronic Jet Classification](https://arxiv.org/abs/2204.03812) [[DOI](https://doi.org/10.1140/epjc/s10052-022-11084-4)]

* [Deep Neural Network Uncertainty Quantification for LArTPC Reconstruction](https://arxiv.org/abs/2302.03787) [[DOI](https://doi.org/10.1088/1748-0221/18/12/P12013)]

* [The DL Advocate: Playing the devil's advocate with hidden systematic uncertainties](https://arxiv.org/abs/2303.15956) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11925-w)]

* [Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties](https://arxiv.org/abs/2312.11676)

* [Calibrating Bayesian Generative Machine Learning for Bayesiamplification](https://arxiv.org/abs/2408.00838)

###  Mitigation

* [Adversarial learning to eliminate systematic errors: a case study in High Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_1.pdf)

* [Machine Learning Uncertainties with Adversarial Neural Networks](https://arxiv.org/abs/1807.08763) [[DOI](https://doi.org/10.1140/epjc/s10052-018-6511-8)]

* [Learning to Pivot with Adversarial Networks](https://arxiv.org/abs/1611.01046) [[url](https://papers.nips.cc/paper/2017/hash/48ab2f9b45957ab574cf005eb8a76760-Abstract.html)]

* [Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks](https://arxiv.org/abs/2102.01078) [[DOI](https://doi.org/10.1007/JHEP04(2021)296)]

* [Improving robustness of jet tagging algorithms with adversarial training](https://arxiv.org/abs/2203.13890) [[DOI](https://doi.org/10.1007/s41781-022-00087-1)]

###  Uncertainty- and inference-aware learning

* [Constraining the Parameters of High-Dimensional Models with Active Learning](https://arxiv.org/abs/1905.08628) [[DOI](https://doi.org/10.1140/epjc/s10052-019-7437-5)]

* [Deep-Learning Jets with Uncertainties and More](https://arxiv.org/abs/1904.10004) [[DOI](https://doi.org/10.21468/SciPostPhys.8.1.006)]

* [INFERNO: Inference-Aware Neural Optimisation](https://arxiv.org/abs/1806.04743) [[DOI](https://doi.org/10.1016/j.cpc.2019.06.007)]

* [Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters](https://arxiv.org/abs/2003.07186) [[DOI](https://doi.org/10.1007/s41781-020-00049-5)]

* [Uncertainty Aware Learning for High Energy Physics](https://arxiv.org/abs/2105.08742) [[DOI](https://doi.org/10.1103/PhysRevD.104.056026)]

* [Punzi-loss: A non-differentiable metric approximation for sensitivity optimisation in the search for new particles](https://arxiv.org/abs/2110.00810) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10070-0)]

* [neos: End-to-End-Optimised Summary Statistics for High Energy Physics](https://arxiv.org/abs/2203.05570) [[DOI](https://doi.org/10.48550/arXiv.2203.05570)]

* [Application of Inferno to a Top Pair Cross Section Measurement with CMS Open Data](https://arxiv.org/abs/2301.10358)

##  Formal Theory and ML

###  Theory and physics for ML

* [Renormalization in the neural network-quantum field theory correspondence](https://arxiv.org/abs/2212.11811)

* [p-Adic statistical field theory and convolutional deep Boltzmann machines](https://arxiv.org/abs/2302.03817) [[DOI](https://doi.org/10.1093/ptep/ptad061)]

* [Structures of Neural Network Effective Theories](https://arxiv.org/abs/2305.02334) [[DOI](https://doi.org/10.1103/PhysRevD.109.105007)]

* [A Correspondence Between Deep Boltzmann Machines and p-Adic Statistical Field Theories](https://arxiv.org/abs/2306.03751)

* [Black holes and the loss landscape in machine learning](https://arxiv.org/abs/2306.14817) [[DOI](https://doi.org/10.1007/JHEP10(2023)107)]

* [Neural Network Field Theories: Non-Gaussianity, Actions, and Locality](https://arxiv.org/abs/2307.03223) [[DOI](https://doi.org/10.1088/2632-2153/ad17d3)]

* [Metric Flows with Neural Networks](https://arxiv.org/abs/2310.19870)

* [Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the Ridgeless Limit](https://arxiv.org/abs/2405.19398)

###  ML for theory

* [Machine Learned Calabi-Yau Metrics and Curvature](https://arxiv.org/abs/2211.09801)

* [Characterizing 4-string contact interaction using machine learning](https://arxiv.org/abs/2211.09129) [[DOI](https://doi.org/10.1007/JHEP04(2024)016)]

* [CYJAX: A package for Calabi-Yau metrics with JAX](https://arxiv.org/abs/2211.12520) [[DOI](https://doi.org/10.1088/2632-2153/acdc84)]

* [Autoencoding heterotic orbifolds with arbitrary geometry](https://arxiv.org/abs/2212.00821) [[DOI](https://doi.org/10.1088/2399-6528/ad246f)]

* [Mahler Measuring the Genetic Code of Amoebae](https://arxiv.org/abs/2212.06553)

* [Clustering Cluster Algebras with Clusters](https://arxiv.org/abs/2212.09771)

* [Machine Learning in Physics and Geometry](https://arxiv.org/abs/2303.12626)

* [The R-mAtrIx Net](https://arxiv.org/abs/2304.07247)

* [Macroscopic Dynamics of Entangled 3+1-Dimensional Systems: A Novel Investigation Into Why My MacBook Cable Tangles in My Backpack Every Single Day](https://arxiv.org/abs/2304.00220)

* [Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6](https://arxiv.org/abs/2307.04891) [[DOI](https://doi.org/10.1016/j.physletb.2023.138266)]

* [Reconstructing $S$-matrix Phases with Machine Learning](https://arxiv.org/abs/2308.09451)

* [Renormalizing Diffusion Models](https://arxiv.org/abs/2308.12355)

* [Scattering with Neural Operators](https://arxiv.org/abs/2308.14789) [[DOI](https://doi.org/10.1103/PhysRevD.108.L101701)]

* [Distilling the essential elements of nuclear binding via neural-network quantum states](https://arxiv.org/abs/2308.16266)

* [Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Duality](https://arxiv.org/abs/2309.05702) [[DOI](https://doi.org/10.1103/PhysRevD.108.106009)]

* [BFBrain: Scalar Bounded-From-Below Conditions from Bayesian Active Learning](https://arxiv.org/abs/2309.10959) [[DOI](https://doi.org/10.1103/PhysRevD.109.095018)]

* [Constructing and Machine Learning Calabi-Yau Five-folds](https://arxiv.org/abs/2310.15966) [[DOI](https://doi.org/10.1002/prop.202300262)]

* [Machine Learning Regularization for the Minimum Volume Formula of Toric Calabi-Yau 3-folds](https://arxiv.org/abs/2310.19276) [[DOI](https://doi.org/10.1103/PhysRevD.109.046015)]

* [Metric Flows with Neural Networks](https://arxiv.org/abs/2310.19870)

* [Seeking Truth and Beauty in Flavor Physics with Machine Learning](https://arxiv.org/abs/2311.00087)

* [Machine learning the breakdown of tame effective theories](https://arxiv.org/abs/2311.03437)

* [Deep learning complete intersection Calabi-Yau manifolds](https://arxiv.org/abs/2311.11847) [[DOI](https://doi.org/10.1142/9781800613706_0005)]

* [Calabi-Yau Four/Five/Six-folds as $\mathbb{P}^n_\textbf{w}$ Hypersurfaces: Machine Learning, Approximation, and Generation](https://arxiv.org/abs/2311.17146) [[DOI](https://doi.org/10.1103/PhysRevD.109.106006)]

* [Autoencoder-Driven Clustering of Intersecting D-brane Models via Tadpole Charge](https://arxiv.org/abs/2312.07181)

* [Computation of Quark Masses from String Theory](https://arxiv.org/abs/2402.01615)

* [NCoder -- A Quantum Field Theory approach to encoding data](https://arxiv.org/abs/2402.00944)

* [Rigor with machine learning from field theory to the Poincar\'e conjecture](https://arxiv.org/abs/2402.13321) [[DOI](https://doi.org/10.1038/s42254-024-00709-0)]

* [Neural Network Learning and Quantum Gravity](https://arxiv.org/abs/2403.03245)

* [Neural network representation of quantum systems](https://arxiv.org/abs/2403.11420)

* [Quantum chaos in the sparse SYK model](https://arxiv.org/abs/2403.13884)

* [Gravitational Duals from Equations of State](https://arxiv.org/abs/2403.14763)

* [Predicting Feynman periods in $\phi^4$-theory](https://arxiv.org/abs/2403.16217)

* [Feynman Diagrams as Computational Graphs](https://arxiv.org/abs/2403.18840)

* [On Machine Learning Complete Intersection Calabi-Yau 3-folds](https://arxiv.org/abs/2404.11710)

* [Classical integrability in the presence of a cosmological constant: analytic and machine learning results](https://arxiv.org/abs/2404.18247)

* [Learning BPS Spectra and the Gap Conjecture](https://arxiv.org/abs/2405.09993)

* [Deep Learning Calabi-Yau four folds with hybrid and recurrent neural network architectures](https://arxiv.org/abs/2405.17406)

##  Experimental results.

 *This section is incomplete as there are many results that directly and indirectly (e.g. via flavor tagging) use modern machine learning techniques.  We will try to highlight experimental results that use deep learning in a critical way for the final analysis sensitivity.*

###  Performance studies

* [Identification of hadronic tau lepton decays using a deep neural network](https://arxiv.org/abs/2201.08458) [[DOI](https://doi.org/10.1088/1748-0221/17/07/P07023)]

* [A feasibility study of multi-electrode high-purity germanium detector for $^{76}$Ge neutrinoless double beta decay searching](https://arxiv.org/abs/2211.06180) [[DOI](https://doi.org/10.1088/1748-0221/18/05/P05025)]

* [Pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals](https://arxiv.org/abs/2211.07892) [[DOI](https://doi.org/10.1088/1748-0221/18/03/P03003)]

* [Deep machine learning for the PANDA software trigger](https://arxiv.org/abs/2211.15390) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11494-y)]

* [Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier](https://arxiv.org/abs/2303.15319) [[DOI](https://doi.org/10.1088/2632-2153/aced7e)]

* [Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network](https://arxiv.org/abs/2311.08885)

* [Neural Network Applications to Improve Drift Chamber Track Position Measurements](https://arxiv.org/abs/2311.15541)

* [Particle identification with machine learning from incomplete data in the ALICE experiment](https://arxiv.org/abs/2403.17436)

* [A Search for Leptonic Photon, $Z_{l}$, at All Three CLIC Energy Stages by Using Artificial Neural Networks (ANN)](https://arxiv.org/abs/2406.10097) [[DOI](https://doi.org/10.5506/APhysPolB.55.6-A4)]

###  Searches and measurements where ML reconstruction is a core component

* [The Full Event Interpretation}: {An Exclusive Tagging Algorithm for the Belle II Experiment](https://arxiv.org/abs/1807.08680) [[DOI](https://doi.org/10.1007/s41781-019-0021-8)]

* [A deep neural network to search for new long-lived particles decaying to jets](https://arxiv.org/abs/1912.12238) [[DOI](https://doi.org/10.1088/2632-2153/ab9023)]

* [Search for an anomalous excess of inclusive charged-current $\nu_e$ interactions in the MicroBooNE experiment using Wire-Cell reconstruction](https://arxiv.org/abs/2110.13978) [[DOI](https://doi.org/10.1103/PhysRevD.105.112005)]

* [Search for an anomalous excess of charged-current quasi-elastic $\nu_e$ interactions with the MicroBooNE experiment using Deep-Learning-based reconstruction](https://arxiv.org/abs/2110.14080) [[DOI](https://doi.org/10.1103/PhysRevD.105.112003)]

* [Search for supersymmetry in final states with missing transverse momentum and three or more b-jets in 139 fb$^{-1}$ of proton\textendash{}proton collisions at $\sqrt{s}](https://arxiv.org/abs/2211.08028) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11543-6)]

* [Search for supersymmetry in final states with a single electron or muon using angular correlations and heavy-object identification in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2211.08476) [[DOI](https://doi.org/10.1007/JHEP09(2023)149)]

* [Search for Higgs Boson and Observation of Z Boson through their Decay into a Charm Quark-Antiquark Pair in Boosted Topologies in Proton-Proton Collisions at s](https://arxiv.org/abs/2211.14181) [[DOI](https://doi.org/10.1103/PhysRevLett.131.041801)]

* [Reconstruction of the event vertex in the PandaX-III experiment with convolution neural network](https://arxiv.org/abs/2211.14992) [[DOI](https://doi.org/10.1007/JHEP05(2023)200)]

* [Measurement of the cross section of top quark-antiquark pair production in association with a W boson in proton-proton collisions at $\sqrt{s}](https://arxiv.org/abs/2212.03770)

* [Evidence for Four-Top Quark Production at the LHC](https://arxiv.org/abs/2212.06075)

* [Search for long-lived particles using out-of-time trackless jets in proton-proton collisions at $ \sqrt{s} $](https://arxiv.org/abs/2212.06695) [[DOI](https://doi.org/10.1007/JHEP07(2023)210)]

* [Search for a new scalar resonance in flavour-changing neutral-current top-quark decays $t \rightarrow qX$ ($q](https://arxiv.org/abs/2301.03902) [[DOI](https://doi.org/10.1007/JHEP07(2023)199)]

* [Search for periodic signals in the dielectron and diphoton invariant mass spectra using 139 fb$^{-1}$ of $pp$ collisions at $\sqrt{s}](https://arxiv.org/abs/2305.10894) [[DOI](https://doi.org/10.1007/JHEP10(2023)079)]

* [Search for a new Z' gauge boson in $4\mu$ events with the ATLAS experiment](https://arxiv.org/abs/2301.09342) [[DOI](https://doi.org/10.1007/JHEP07(2023)090)]

* [Observation of single-top-quark production in association with a photon using the ATLAS detector](https://arxiv.org/abs/2302.01283) [[DOI](https://doi.org/10.1103/PhysRevLett.131.181901)]

* [Search for a light charged Higgs boson in $t \rightarrow H^{\pm}b$ decays, with $H^{\pm} \rightarrow cb$, in the lepton+jets final state in proton-proton collisions at $\sqrt{s}](https://arxiv.org/abs/2302.11739) [[DOI](https://doi.org/10.1007/JHEP09(2023)004)]

* [Search for third-generation vector-like leptons in $pp$ collisions at $\sqrt{s}](https://arxiv.org/abs/2303.05441) [[DOI](https://doi.org/10.1007/JHEP07(2023)118)]

* [Evidence of off-shell Higgs boson production from $ZZ$ leptonic decay channels and constraints on its total width with the ATLAS detector](https://arxiv.org/abs/2304.01532) [[DOI](https://doi.org/10.1016/j.physletb.2023.138223)]

* [Measurement of \ensuremath{\nu}\ensuremath{\mu} charged-current inclusive \ensuremath{\pi}0 production in the NOvA near detector](https://arxiv.org/abs/2306.04028) [[DOI](https://doi.org/10.1103/PhysRevD.107.112008)]

* [Searches for supersymmetric particles with prompt decays with the ATLAS detector](https://arxiv.org/abs/2306.15014)

* [Applying Machine Learning Techniques to Searches for Lepton-Partner Pair-Production with Intermediate Mass Gaps at the Large Hadron Collider](https://arxiv.org/abs/2309.10197) [[DOI](https://doi.org/10.1103/PhysRevD.109.075018)]

* [Boosting dark matter searches at muon colliders with Machine Learning: the mono-Higgs channel as a case study](https://arxiv.org/abs/2309.11241) [[DOI](https://doi.org/10.1093/ptep/ptad144)]

* [Suppression of Neutron Background using Deep Neural Network and Fourier Frequency Analysis at the KOTO Experiment](https://arxiv.org/abs/2309.12063) [[DOI](https://doi.org/10.1016/j.nima.2023.169010)]

* [Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC](https://arxiv.org/abs/2309.12417) [[DOI](https://doi.org/10.1051/epjconf/202429509003)]

* [Novel techniques for alpha/beta pulse shape discrimination in Borexino](https://arxiv.org/abs/2310.11826)

* [CMS highlights on searches for new physics in final states with jets](https://arxiv.org/abs/2401.07172) [[DOI](https://doi.org/10.22323/1.450.0162)]

* [Search for new phenomena with top-quark pairs and large missing transverse momentum using 140 fb$^{-1}$ of pp collision data at $ \sqrt{s} $](https://arxiv.org/abs/2401.13430) [[DOI](https://doi.org/10.1007/JHEP03(2024)139)]

* [Search for long-lived particles using displaced vertices and missing transverse momentum in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2402.15804)

* [Observation of electroweak production of $W^+W^-$ in association with jets in proton-proton collisions at $\sqrt{s}](https://arxiv.org/abs/2403.04869)

* [Exploration at the high-energy frontier: ATLAS Run 2 searches investigating the exotic jungle beyond the Standard Model](https://arxiv.org/abs/2403.09292)

* [Search for Higgs Boson Pair Production with One Associated Vector Boson in Proton-Proton Collisions at $\sqrt{s}$](https://arxiv.org/abs/2404.08462)

* [Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons in proton-proton collisions at a center of mass energy of 13 TeV with the ATLAS detector](https://arxiv.org/abs/2404.12915)

* [Search for new resonances decaying to pairs of merged diphotons in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2405.00834)

* [ATLAS searches for additional scalars and exotic Higgs boson decays with the LHC Run 2 dataset](https://arxiv.org/abs/2405.04914)

* [Test of light-lepton universality in $\tau$ decays with the Belle II experiment](https://arxiv.org/abs/2405.14625)

* [Dark sector searches with the CMS experiment](https://arxiv.org/abs/2405.13778)

* [A simultaneous unbinned differential cross section measurement of twenty-four $Z$+jets kinematic observables with the ATLAS detector](https://arxiv.org/abs/2405.20041)

* [Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE](https://arxiv.org/abs/2406.10123)

* [Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning](https://arxiv.org/abs/2407.00178)

* [Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H $\to$$\mathrm{b\bar{b}}$ decay mode using LHC proton-proton collision data at $\sqrt{s}$](https://arxiv.org/abs/2407.08012)

* [Accuracy versus precision in boosted top tagging with the ATLAS detector](https://arxiv.org/abs/2407.20127)

###  Final analysis discriminate for searches

* [Search for non-resonant Higgs boson pair production in the $bb\ell\nu\ell\nu$ final state with the ATLAS detector in $pp$ collisions at $\sqrt{s}](https://arxiv.org/abs/1908.06765) [[DOI](https://doi.org/10.1016/j.physletb.2019.135145)]

* [Search for Higgs boson decays into a $Z$ boson and a light hadronically decaying resonance using 13 TeV $pp$ collision data from the ATLAS detector](https://arxiv.org/abs/2004.01678) [[DOI](https://doi.org/10.1103/PhysRevLett.125.221802)]

* [Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector](https://arxiv.org/abs/2005.02983) [[DOI](https://doi.org/10.1103/PhysRevLett.125.131801)]

* [Inclusive search for highly boosted Higgs bosons decaying to bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s}](https://arxiv.org/abs/2006.13251) [[DOI](https://doi.org/10.1007/JHEP12(2020)085)]

* [Evidence for Four-Top Quark Production at the LHC](https://arxiv.org/abs/2212.06075)

###  Measurements using deep learning directly (not through object reconstruction)

* [Measurement of lepton-jet correlation in deep-inelastic scattering with the H1 detector using machine learning for unfolding](https://arxiv.org/abs/2108.12376) [[DOI](https://doi.org/10.1103/PhysRevLett.128.132002)]

* [Unbinned Deep Learning Jet Substructure Measurement in High $Q^2$ ep collisions at HERA](https://arxiv.org/abs/2303.13620) [[DOI](https://doi.org/10.1016/j.physletb.2023.138101)]