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 (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-10-29T11:37:53.000Z (over 1 year ago)
- Last Synced: 2024-10-29T13:26:18.531Z (over 1 year ago)
- Topics: hep, latex, machine-learning, papers, particle-physics
- Language: TeX
- Homepage: https://iml-wg.github.io/HEPML-LivingReview
- Size: 2.06 MB
- Stars: 353
- Watchers: 28
- Forks: 107
- Open Issues: 17
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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.*
[](https://iml-wg.github.io/HEPML-LivingReview/assets/hepml_review.pdf)
[](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, and Ramon Winterhalder.
## Reviews
### Modern reviews
* [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://arxiv.org/abs/2209.07559) (2022)
* [Artificial Intelligence and Machine Learning in Nuclear Physics](https://arxiv.org/abs/2112.02309) [[DOI](https://doi.org/10.1103/RevModPhys.94.031003)] (2021)
* [Machine Learning in the Search for New Fundamental Physics](https://arxiv.org/abs/2112.03769) (2021)
* [Modern Machine Learning and Particle Physics](https://arxiv.org/abs/2103.12226) [[DOI](https://doi.org/10.1162/99608f92.beeb1183)] (2021)
* [Machine and Deep Learning Applications in Particle Physics](https://arxiv.org/abs/1912.08245) [[DOI](https://doi.org/10.1142/S0217751X19300199)] (2019)
* [Machine learning and the physical sciences](https://arxiv.org/abs/1903.10563) [[DOI](https://doi.org/10.1103/RevModPhys.91.045002)] (2019)
* [Machine learning at the energy and intensity frontiers of particle physics](https://doi.org/10.1038/s41586-018-0361-2) (2018)
* [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)] (2018)
* [Deep Learning and its Application to LHC Physics](https://arxiv.org/abs/1806.11484) [[DOI](https://doi.org/10.1146/annurev-nucl-101917-021019)] (2018)
* [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)] (2017)
### Specialized reviews
* [Deep Learning and Model Independence](https://arxiv.org/abs/2507.03438) (2025)
* [Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb](https://arxiv.org/abs/2506.14578) (2025)
* [Lecture Notes on Normalizing Flows for Lattice Quantum Field Theories](https://arxiv.org/abs/2504.18126) (2025)
* [What is AI, what is it not, how we use it in physics and how it impacts... you](https://arxiv.org/abs/2504.01827) (2025)
* [Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF](https://arxiv.org/abs/2503.14192) (2025)
* [Machine-learning approaches to accelerating lattice simulations](https://arxiv.org/abs/2502.02670) [[DOI](https://doi.org/10.22323/1.466.0010)] (2025)
* [Run 3 performance and advances in heavy-flavor jet tagging in CMS](https://arxiv.org/abs/2412.05863) [[DOI](https://doi.org/10.22323/1.476.0992)] (2024)
* [CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation](https://arxiv.org/abs/2410.21611) (2024)
* [Exploring jets: substructure and flavour tagging in CMS and ATLAS](https://arxiv.org/abs/2410.14330) [[DOI](https://doi.org/10.22323/1.478.0150)] (2024)
* [Novel machine learning applications at the LHC](https://arxiv.org/abs/2409.20413) [[DOI](https://doi.org/10.22323/1.476.0012)] (2024)
* [Unveiling the Secrets of New Physics Through Top Quark Tagging](https://arxiv.org/abs/2409.12085) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01257-5)] (2024)
* [TASI Lectures on Physics for Machine Learning](https://arxiv.org/abs/2408.00082) (2024)
* [QCD Masterclass Lectures on Jet Physics and Machine Learning](https://arxiv.org/abs/2407.04897) [[DOI](https://doi.org/10.1140/epjc/s10052-024-13341-0)] (2024)
* [Top-philic Machine Learning](https://arxiv.org/abs/2407.00183) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01237-9)] (2024)
* [A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation](https://arxiv.org/abs/2406.12898) (2024)
* [The Landscape of Unfolding with Machine Learning](https://arxiv.org/abs/2404.18807) [[DOI](https://doi.org/10.21468/SciPostPhys.18.2.070)] (2024)
* [Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC](https://arxiv.org/abs/2404.01071) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01234-y)] (2024)
* [Unsupervised and lightly supervised learning in particle physics](https://arxiv.org/abs/2403.13676) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01235-x)] (2024)
* [High-energy physics image classification: A Survey of Jet Applications](https://arxiv.org/abs/2403.11934) [[DOI](https://doi.org/10.15302/frontphys.2025.035301)] (2024)
* [The SMARTHEP European Training Network](https://arxiv.org/abs/2401.13484) [[DOI](https://doi.org/10.1051/epjconf/202429508022)] (2024)
* [Les Houches guide to reusable ML models in LHC analyses](https://arxiv.org/abs/2312.14575) [[DOI](https://doi.org/10.21468/SciPostPhysCommRep.3)] (2023)
* [Machine Learning for Anomaly Detection in Particle Physics](https://arxiv.org/abs/2312.14190) [[DOI](https://doi.org/10.1016/j.revip.2024.100091)] (2023)
* [Deep Generative Models for Detector Signature Simulation: An Analytical Taxonomy](https://arxiv.org/abs/2312.09597) [[DOI](https://doi.org/10.1016/j.revip.2024.100092)] (2023)
* [Artificial Intelligence for the Electron Ion Collider (AI4EIC)](https://arxiv.org/abs/2307.08593) [[DOI](https://doi.org/10.1007/s41781-024-00113-4)] (2023)
* [Overview: Jet quenching with machine learning](https://arxiv.org/abs/2308.10035) (2023)
* [Graph neural networks at the Large Hadron Collider](https://doi.org/10.1038/s42254-023-00569-0) (2023)
* [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)] (2023)
* [Snowmass Neutrino Frontier Report](https://arxiv.org/abs/2211.08641) (2022)
* [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)] (2022)
* [Bridging Machine Learning and Sciences: Opportunities and Challenges](https://arxiv.org/abs/2210.13441) (2022)
* [Modern Machine Learning for LHC Physicists](https://arxiv.org/abs/2211.01421) (2022)
* [Interpretable Uncertainty Quantification in AI for HEP](https://arxiv.org/abs/2208.03284) [[DOI](https://doi.org/10.2172/1886020)] (2022)
* [Data Science and Machine Learning in Education](https://arxiv.org/abs/2207.09060) (2022)
* [Boosted decision trees](https://arxiv.org/abs/2206.09645) [[DOI](https://doi.org/10.1142/9789811234033_0002)] (2022)
* [Physics Community Needs, Tools, and Resources for Machine Learning](https://arxiv.org/abs/2203.16255) (2022)
* [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://arxiv.org/abs/2203.12852) (2022)
* [New directions for surrogate models and differentiable programming for High Energy Physics detector simulation](https://arxiv.org/abs/2203.08806) (2022)
* [Machine Learning and Cosmology](https://arxiv.org/abs/2203.08056) (2022)
* [Machine Learning and LHC Event Generation](https://arxiv.org/abs/2203.07460) [[DOI](https://doi.org/10.21468/SciPostPhys.14.4.079)] (2022)
* [Symmetry Group Equivariant Architectures for Physics](https://arxiv.org/abs/2203.06153) (2022)
* [Solving Simulation Systematics in and with AI/ML](https://arxiv.org/abs/2203.06112) (2022)
* [Deep Learning From Four Vectors](https://arxiv.org/abs/2203.03067) (2022)
* [A survey of machine learning-based physics event generation](https://arxiv.org/abs/2106.00643) [[DOI](https://doi.org/10.24963/ijcai.2021/588)] (2021)
* [Sequence-based Machine Learning Models in Jet Physics](https://arxiv.org/abs/2102.06128) (2021)
* [Quantum Machine Learning in High Energy Physics](https://arxiv.org/abs/2005.08582) [[DOI](https://doi.org/10.1088/2632-2153/abc17d)] (2020)
* [Image-Based Jet Analysis](https://arxiv.org/abs/2012.09719) (2020)
* [Machine Learning scientific competitions and datasets](https://arxiv.org/abs/2012.08520) (2020)
* [The frontier of simulation-based inference](https://arxiv.org/abs/1911.01429) [[DOI](https://doi.org/10.1073/pnas.1912789117)] (2019)
* [Distributed Training and Optimization Of Neural Networks](https://arxiv.org/abs/2012.01839) [[DOI](https://doi.org/10.1142/9789811234033_0008)] (2020)
* [Graph Neural Networks for Particle Tracking and Reconstruction](https://arxiv.org/abs/2012.01249) [[DOI](https://doi.org/10.1142/9789811234033_0012)] (2020)
* [Anomaly Detection for Physics Analysis and Less than Supervised Learning](https://arxiv.org/abs/2010.14554) (2020)
* [Simulation-based inference methods for particle physics](https://arxiv.org/abs/2010.06439) (2020)
* [Parton distribution functions](https://arxiv.org/abs/2008.12305) (2020)
* [Generative Networks for LHC events](https://arxiv.org/abs/2008.08558) (2020)
* [A Review on Machine Learning for Neutrino Experiments](https://arxiv.org/abs/2008.01242) [[DOI](https://doi.org/10.1142/S0217751X20430058)] (2020)
* [Graph Neural Networks in Particle Physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/10.1088/2632-2153/abbf9a)] (2020)
* [Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review](https://arxiv.org/abs/2007.09121) (2020)
* [The Machine Learning Landscape of Top Taggers](https://arxiv.org/abs/1902.09914) [[DOI](https://doi.org/10.21468/SciPostPhys.7.1.014)] (2019)
### Classical papers
* [Finding Gluon Jets With a Neural Trigger](https://doi.org/10.1103/PhysRevLett.65.1321) (1990)
* [Neural Networks and Cellular Automata in Experimental High-energy Physics](https://doi.org/10.1016/0010-4655(88)90004-5) (1988)
### Datasets
* [Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics](https://arxiv.org/abs/2412.10504) (2024)
* [CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation](https://arxiv.org/abs/2410.21611) (2024)
* [FAIR Universe HiggsML Uncertainty Challenge Competition](https://arxiv.org/abs/2410.02867) (2024)
* [RODEM Jet Datasets](https://arxiv.org/abs/2408.11616) (2024)
* [Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype](https://arxiv.org/abs/2309.06582) (2023)
* [Public Kaggle Competition ''IceCube -- Neutrinos in Deep Ice''](https://arxiv.org/abs/2307.15289) (2023)
* [Particle Transformer for Jet Tagging](https://arxiv.org/abs/2202.03772) (2022)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
## Classification
### Parameterized classifiers
* [Mass-unspecific classifiers for mass-dependent searches](https://arxiv.org/abs/2503.20926) (2025)
* [Boosting likelihood learning with event reweighting](https://arxiv.org/abs/2308.05704) [[DOI](https://doi.org/10.1007/JHEP03(2024)117)] (2023)
* [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)] (2021)
* [Approximating Likelihood Ratios with Calibrated Discriminative Classifiers](https://arxiv.org/abs/1506.02169) (2015)
* [Parameterized neural networks for high-energy physics](https://arxiv.org/abs/1601.07913) [[DOI](https://doi.org/10.1140/epjc/s10052-016-4099-4)] (2016)
### Representations
#### Jet images
* [High-energy physics image classification: A Survey of Jet Applications](https://arxiv.org/abs/2403.11934) [[DOI](https://doi.org/10.15302/frontphys.2025.035301)] (2024)
* [A Guide to Diagnosing Colored Resonances at Hadron Colliders](https://arxiv.org/abs/2306.00079) [[DOI](https://doi.org/10.1007/JHEP08(2023)173)] (2023)
* [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)] (2023)
* [Identifying the Quantum Properties of Hadronic Resonances using Machine Learning](https://arxiv.org/abs/2105.04582) [[DOI](https://doi.org/10.21468/SciPostPhysCore.8.2.039)] (2021)
* [Deep learning jet modifications in heavy-ion collisions](https://arxiv.org/abs/2012.07797) [[DOI](https://doi.org/10.1007/JHEP03(2021)206)] (2020)
* [Learning to Isolate Muons](https://arxiv.org/abs/2102.02278) [[DOI](https://doi.org/10.1007/JHEP10(2021)200)] (2021)
* [Quark-Gluon Jet Discrimination Using Convolutional Neural Networks](https://arxiv.org/abs/2012.02531) [[DOI](https://doi.org/10.3938/jkps.74.219)] (2020)
* [An Attention Based Neural Network for Jet Tagging](https://arxiv.org/abs/2009.00170) (2020)
* [Reconstructing boosted Higgs jets from event image segmentation](https://arxiv.org/abs/2008.13529) [[DOI](https://doi.org/10.1007/JHEP04(2021)156)] (2020)
* [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)] (2018)
* [Deep-learning Top Taggers or The End of QCD?](https://arxiv.org/abs/1701.08784) [[DOI](https://doi.org/10.1007/JHEP05(2017)006)] (2017)
* [Deep learning in color: towards automated quark/gluon](https://arxiv.org/abs/1612.01551) [[DOI](https://doi.org/10.1007/JHEP01(2017)110)] (2016)
* [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)] (2016)
* [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)] (2018)
* [Boosting $H\to b\bar b$ with Machine Learning](https://arxiv.org/abs/1807.10768) [[DOI](https://doi.org/10.1007/JHEP10(2018)101)] (2018)
* [Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector](http://cds.cern.ch/record/2275641) (2017)
* [Jet-images — deep learning edition](https://arxiv.org/abs/1511.05190) [[DOI](https://doi.org/10.1007/JHEP07(2016)069)] (2015)
* [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)] (2015)
* [Jet-Images: Computer Vision Inspired Techniques for Jet Tagging](https://arxiv.org/abs/1407.5675) [[DOI](https://doi.org/10.1007/JHEP02(2015)118)] (2014)
* [How to tell quark jets from gluon jets](https://doi.org/10.1103/PhysRevD.44.2025) (1991)
#### Event images
* [Fast Low Energy Reconstruction using Convolutional Neural Networks](https://arxiv.org/abs/2505.16777) (2025)
* [Deep Learning to Improve the Sensitivity of Higgs Pair Searches in the $4b$ Channel at the LHC](https://arxiv.org/abs/2505.04496) (2025)
* [Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment](https://arxiv.org/abs/2503.06727) (2025)
* [Simultaneous Estimation of Elliptic Flow Coefficient and Impact Parameter in Heavy-Ion Collisions using CNN](https://arxiv.org/abs/2411.11001) (2024)
* [A novel machine learning method to detect double-$\Lambda$ hypernuclear events in nuclear emulsions](https://arxiv.org/abs/2409.01657) [[DOI](https://doi.org/10.1016/j.nima.2024.170196)] (2024)
* [Exploring the Synergy of Kinematics and Dynamics for Collider Physics](https://arxiv.org/abs/2311.16674) [[DOI](https://doi.org/10.1103/PhysRevD.110.115035)] (2023)
* [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)] (2023)
* [Large-Scale Deep Learning for Multi-Jet Event Classification](https://arxiv.org/abs/2207.11710) (2022)
* [Jet Single Shot Detection](https://arxiv.org/abs/2105.05785) [[DOI](https://doi.org/10.1051/epjconf/202125104027)] (2021)
* [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)] (2021)
* [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)] (2019)
* [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)] (2020)
* [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)] (2018)
* [Boosting $H\to b\bar b$ with Machine Learning](https://arxiv.org/abs/1807.10768) [[DOI](https://doi.org/10.1007/JHEP10(2018)101)] (2018)
* [Convolutional Neural Networks with Event Images for Pileup Mitigation with the ATLAS Detector](http://cds.cern.ch/record/2684070) (2019)
* [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)] (2018)
#### Sequences
* [Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment](http://cdsweb.cern.ch/record/2255226) (2017)
* [Sequence-based Machine Learning Models in Jet Physics](https://arxiv.org/abs/2102.06128) (2021)
* [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)] (2021)
* [Jet Flavour Classification Using DeepJet](https://arxiv.org/abs/2008.10519) [[DOI](https://doi.org/10.1088/1748-0221/15/12/P12012)] (2020)
* [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)] (2018)
* [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)] (2016)
#### Trees
* [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) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01308-x)] (2024)
* [Photon Classification with Gradient Boosted Trees at CLAS12](https://arxiv.org/abs/2402.13105) [[DOI](https://doi.org/10.1088/1748-0221/19/06/C06006)] (2024)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2021)
* [Recursive Neural Networks in Quark/Gluon Tagging](https://arxiv.org/abs/1711.02633) [[DOI](https://doi.org/10.1007/s41781-018-0007-y)] (2017)
* [QCD-Aware Recursive Neural Networks for Jet Physics](https://arxiv.org/abs/1702.00748) [[DOI](https://doi.org/10.1007/JHEP01(2019)057)] (2017)
#### Graphs
* [Reconstruction of cosmic-ray properties with GNN in GRAND](https://arxiv.org/abs/2507.07541) (2025)
* [Replacing detector simulation with heterogeneous GNNs in flavour physics analyses](https://arxiv.org/abs/2507.05069) (2025)
* [Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining](https://arxiv.org/abs/2507.05099) (2025)
* [Direct Vertex Reconstruction of $\Lambda$ Baryons from Hits in CLAS12 using Graph Neural Networks](https://arxiv.org/abs/2507.01868) (2025)
* [Graph theory inspired anomaly detection at the LHC](https://arxiv.org/abs/2506.19920) (2025)
* [Guided Graph Compression for Quantum Graph Neural Networks](https://arxiv.org/abs/2506.09862) (2025)
* [Physics and Computing Performance of the EggNet Tracking Pipeline](https://arxiv.org/abs/2506.03415) (2025)
* [Hybrid-Graph Neural Network Method for Muon Fast Reconstruction in Neutrino Telescopes](https://arxiv.org/abs/2505.23425) (2025)
* [Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant](https://arxiv.org/abs/2505.20280) (2025)
* [Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks](https://arxiv.org/abs/2504.21844) (2025)
* [Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with Machine Learning](https://arxiv.org/abs/2504.11791) (2025)
* [Classification of Electron and Muon Neutrino Events for the ESS$\nu$SB Near Water Cherenkov Detector using Graph Neural Networks](https://arxiv.org/abs/2503.15247) (2025)
* [Application of Geometric Deep Learning for Tracking of Hyperons in a Straw Tube Detector](https://arxiv.org/abs/2503.14305) (2025)
* [Graph-based Full Event Interpretation: a graph neural network for event reconstruction in Belle II](https://arxiv.org/abs/2503.09401) (2025)
* [Machine Learning for Single-Ended Event Reconstruction in PROSPECT Experiment](https://arxiv.org/abs/2503.06727) (2025)
* [Graph Neural Network Flavor Tagger and measurement of $\mathrm{sin}2\beta$ at Belle II](https://arxiv.org/abs/2501.17631) (2025)
* [Evaluating the Impact of Detector Design on Jet Flavor Tagging for Future Colliders](https://arxiv.org/abs/2501.16584) (2025)
* [Pretrained Event Classification Model for High Energy Physics Analysis](https://arxiv.org/abs/2412.10665) (2024)
* [End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II](https://arxiv.org/abs/2411.13596) [[DOI](https://doi.org/10.1007/s41781-025-00135-6)] (2024)
* [HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction](https://arxiv.org/abs/2410.23236) (2024)
* [Observation of a rare beta decay of the charmed baryon with a Graph Neural Network](https://arxiv.org/abs/2410.13515) [[DOI](https://doi.org/10.1038/s41467-024-55042-y)] (2024)
* [Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC](https://arxiv.org/abs/2410.04465) [[DOI](https://doi.org/10.1088/1674-1137/adacc5)] (2024)
* [Search for light long-lived particles decaying to displaced jets in proton-proton collisions at $\sqrt{s}$](https://arxiv.org/abs/2409.10806) [[DOI](https://doi.org/10.1088/1361-6633/adaa13)] (2024)
* [EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction](https://arxiv.org/abs/2407.13925) (2024)
* [Graph Neural Network-Based Track Finding in the LHCb Vertex Detector](https://arxiv.org/abs/2407.12119) [[DOI](https://doi.org/10.1088/1748-0221/19/12/P12022)] (2024)
* [Accelerating Graph-based Tracking Tasks with Symbolic Regression](https://arxiv.org/abs/2406.16752) [[DOI](https://doi.org/10.1088/2632-2153/ad8f12)] (2024)
* [Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter](https://arxiv.org/abs/2406.11937) [[DOI](https://doi.org/10.1088/1748-0221/19/11/P11025)] (2024)
* [Advancing Set-Conditional Set Generation: Graph Diffusion for Fast Simulation of Reconstructed Particles](https://arxiv.org/abs/2405.10106) [[DOI](https://doi.org/10.1103/PhysRevD.110.092013)] (2024)
* [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872) [[DOI](https://doi.org/10.1103/PhysRevD.110.032008)] (2024)
* [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) [[DOI](https://doi.org/10.1007/s41781-024-00122-3)] (2024)
* [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) [[DOI](https://doi.org/10.1103/PhysRevD.110.012001)] (2024)
* [Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning](https://arxiv.org/abs/2402.10149) [[DOI](https://doi.org/10.1103/PhysRevD.111.032004)] (2024)
* [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)] (2024)
* [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)] (2024)
* [Combined track finding with GNN \& CKF](https://arxiv.org/abs/2401.16016) (2024)
* [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)] (2024)
* [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)] (2023)
* [Graph Structure from Point Clouds: Geometric Attention is All You Need](https://arxiv.org/abs/2307.16662) (2023)
* [LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC](https://arxiv.org/abs/2308.13611) (2023)
* [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)] (2023)
* [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)] (2023)
* [Flavour tagging with graph neural networks with the ATLAS detector](https://arxiv.org/abs/2306.04415) (2023)
* [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)] (2023)
* [Hierarchical Graph Neural Networks for Particle Track Reconstruction](https://arxiv.org/abs/2303.01640) (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events](https://arxiv.org/abs/2307.06434) (2023)
* [Equivariant Graph Neural Networks for Charged Particle Tracking](https://arxiv.org/abs/2304.05293) (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Do graph neural networks learn traditional jet substructure?](https://arxiv.org/abs/2211.09912) (2022)
* [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)] (2022)
* [Climbing four tops with graph networks, transformers and pairwise features](https://arxiv.org/abs/2211.05143) (2022)
* [PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics](https://arxiv.org/abs/2211.00454) (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://arxiv.org/abs/2112.02048) [[DOI](https://doi.org/10.3389/fdata.2022.828666)] (2021)
* [Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance](https://arxiv.org/abs/2111.12849) (2021)
* [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)] (2021)
* [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)] (2021)
* [Anomaly detection with Convolutional Graph Neural Networks](https://arxiv.org/abs/2105.07988) [[DOI](https://doi.org/10.1007/JHEP08(2021)080)] (2021)
* [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)] (2021)
* [Graph Generative Models for Fast Detector Simulations in High Energy Physics](https://arxiv.org/abs/2104.01725) (2021)
* [Jet characterization in Heavy Ion Collisions by QCD-Aware Graph Neural Networks](https://arxiv.org/abs/2103.14906) (2021)
* [Charged particle tracking via edge-classifying interaction networks](https://arxiv.org/abs/2103.16701) [[DOI](https://doi.org/10.1007/s41781-021-00073-z)] (2021)
* [Instance Segmentation GNNs for One-Shot Conformal Tracking at the LHC](https://arxiv.org/abs/2103.06509) (2021)
* [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)] (2021)
* [Deep Learning strategies for ProtoDUNE raw data denoising](https://arxiv.org/abs/2103.01596) [[DOI](https://doi.org/10.1007/s41781-021-00077-9)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)] (2020)
* [Particle Track Reconstruction using Geometric Deep Learning](https://arxiv.org/abs/2012.08515) (2020)
* [Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs](https://arxiv.org/abs/2012.01563) (2020)
* [The Boosted Higgs Jet Reconstruction via Graph Neural Network](https://arxiv.org/abs/2010.05464) [[DOI](https://doi.org/10.1103/PhysRevD.103.116025)] (2020)
* [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)] (2020)
* [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://arxiv.org/abs/2007.00149) (2020)
* [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)] (2020)
* [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)] (2020)
* [Graph Neural Networks in Particle Physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/10.1088/2632-2153/abbf9a)] (2020)
* [Casting a graph net to catch dark showers](https://arxiv.org/abs/2006.08639) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.046)] (2020)
* [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)] (2020)
* [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)] (2020)
* [Towards a Computer Vision Particle Flow](https://arxiv.org/abs/2003.08863) [[DOI](https://doi.org/10.1140/epjc/s10052-021-08897-0)] (2020)
* [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)] (2019)
* [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)] (2019)
* [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)] (2019)
* [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)] (2019)
* [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)] (2018)
* [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)] (2018)
* [Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors](https://arxiv.org/abs/2003.11603) (2020)
* [Neural Message Passing for Jet Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_29.pdf) (2017)
#### Sets (point clouds)
* [Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2504.08182) (2025)
* [Neutrino type identification for atmospheric neutrinos in a large homogeneous liquid scintillation detector](https://arxiv.org/abs/2503.21353) (2025)
* [Product Manifold Machine Learning for Physics](https://arxiv.org/abs/2412.07033) (2024)
* [Point cloud-based diffusion models for the Electron-Ion Collider](https://arxiv.org/abs/2410.22421) (2024)
* [Is Tokenization Needed for Masked Particle Modelling?](https://arxiv.org/abs/2409.12589) (2024)
* [Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling](https://arxiv.org/abs/2403.08854) [[DOI](https://doi.org/10.1103/PhysRevD.110.074020)] (2024)
* [Sets are All You Need: Ultrafast Jet Classification on FPGAs for HL-LHC](https://arxiv.org/abs/2402.01876) [[DOI](https://doi.org/10.1088/2632-2153/ad5f10)] (2024)
* [Multi-scale cross-attention transformer encoder for event classification](https://arxiv.org/abs/2401.00452) [[DOI](https://doi.org/10.1007/JHEP03(2024)144)] (2024)
* [PAIReD jet: A multi-pronged resonance tagging strategy across all Lorentz boosts](https://arxiv.org/abs/2311.11011) [[DOI](https://doi.org/10.1007/JHEP09(2024)128)] (2023)
* [The Optimal use of Segmentation for Sampling Calorimeters](https://arxiv.org/abs/2310.04442) [[DOI](https://doi.org/10.1088/1748-0221/19/06/P06002)] (2023)
* [EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion](https://arxiv.org/abs/2310.00049) (2023)
* [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)] (2023)
* [Attention to Mean-Fields for Particle Cloud Generation](https://arxiv.org/abs/2305.15254) (2023)
* [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979) [[DOI](https://doi.org/10.1007/JHEP07(2024)257)] (2023)
* [Comparing Point Cloud Strategies for Collider Event Classification](https://arxiv.org/abs/2212.10659) [[DOI](https://doi.org/10.1103/PhysRevD.108.012001)] (2022)
* [Point Cloud Generation using Transformer Encoders and Normalising Flows](https://arxiv.org/abs/2211.13623) (2022)
* [Particle Transformer for Jet Tagging](https://arxiv.org/abs/2202.03772) (2022)
* [Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS](https://cds.cern.ch/record/2718948) (2020)
* [Particle Convolution for High Energy Physics](https://arxiv.org/abs/2107.02908) (2021)
* [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)] (2021)
* [Point Cloud Transformers applied to Collider Physics](https://arxiv.org/abs/2102.05073) [[DOI](https://doi.org/10.1088/2632-2153/ac07f6)] (2021)
* [Learning to Isolate Muons](https://arxiv.org/abs/2102.02278) [[DOI](https://doi.org/10.1007/JHEP10(2021)200)] (2021)
* [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)] (2020)
* [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)] (2020)
* [Equivariant Energy Flow Networks for Jet Tagging](https://arxiv.org/abs/2012.00964) [[DOI](https://doi.org/10.1103/PhysRevD.103.074022)] (2020)
* [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)] (2020)
* [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)] (2020)
* [ParticleNet: Jet Tagging via Particle Clouds](https://arxiv.org/abs/1902.08570) [[DOI](https://doi.org/10.1103/PhysRevD.101.056019)] (2019)
* [Energy Flow Networks: Deep Sets for Particle Jets](https://arxiv.org/abs/1810.05165) [[DOI](https://doi.org/10.1007/JHEP01(2019)121)] (2018)
#### Physics-inspired basis
* [Charm-hadron reconstruction through three body decay in hadronic collisions using Machine Learning](https://arxiv.org/abs/2504.18279) (2025)
* [Machine Learning-Based b-Jet Tagging in pp Collisions at $\sqrt{s}](https://arxiv.org/abs/2504.18291) (2025)
* [Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks](https://arxiv.org/abs/2410.14760) (2024)
* [Universal New Physics Latent Space](https://arxiv.org/abs/2407.20315) [[DOI](https://doi.org/10.1103/PhysRevD.111.016006)] (2024)
* [Physics-informed machine learning approaches to reactor antineutrino detection](https://arxiv.org/abs/2407.06139) (2024)
* [Exotic and physics-informed support vector machines for high energy physics](https://arxiv.org/abs/2407.03538) (2024)
* [Exploring the Truth and Beauty of Theory Landscapes with Machine Learning](https://arxiv.org/abs/2401.11513) [[DOI](https://doi.org/10.1016/j.physletb.2024.138941)] (2024)
* [JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables](https://arxiv.org/abs/2311.14654) (2023)
* [Jet Rotational Metrics](https://arxiv.org/abs/2311.06686) [[DOI](https://doi.org/10.1007/JHEP08(2024)049)] (2023)
* [Learning Broken Symmetries with Resimulation and Encouraged Invariance](https://arxiv.org/abs/2311.05952) (2023)
* [Retrieval of Boost Invariant Symbolic Observables via Feature Importance](https://arxiv.org/abs/2306.13496) (2023)
* [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)] (2023)
* [Decay-aware neural network for event classification in collider physics](https://arxiv.org/abs/2212.08759) (2022)
* [Resurrecting $b\bar{b}h$ with kinematic shapes](https://arxiv.org/abs/2011.13945) [[DOI](https://doi.org/10.1007/JHEP04(2021)139)] (2020)
* [Deep-learned Top Tagging with a Lorentz Layer](https://arxiv.org/abs/1707.08966) [[DOI](https://doi.org/10.21468/SciPostPhys.5.3.028)] (2017)
* [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)] (2017)
* [Novel Jet Observables from Machine Learning](https://arxiv.org/abs/1710.01305) [[DOI](https://doi.org/10.1007/JHEP03(2018)086)] (2017)
* [How Much Information is in a Jet?](https://arxiv.org/abs/1704.08249) [[DOI](https://doi.org/10.1007/JHEP06(2017)073)] (2017)
* [Automating the Construction of Jet Observables with Machine Learning](https://arxiv.org/abs/1902.07180) [[DOI](https://doi.org/10.1103/PhysRevD.100.095016)] (2019)
### Targets
#### $W/Z$ tagging
* [Jet Reconstruction with Mamba Networks in Collider Events](https://arxiv.org/abs/2506.18336) (2025)
* [Proposed measurement of longitudinally polarised vector bosons in $WH$ and $ZH$ production at Hadron colliders](https://arxiv.org/abs/2506.13002) (2025)
* [Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC](https://arxiv.org/abs/2410.04465) [[DOI](https://doi.org/10.1088/1674-1137/adacc5)] (2024)
* [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)] (2024)
* [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)] (2023)
* [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) (2023)
* [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)] (2023)
* [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979) [[DOI](https://doi.org/10.1007/JHEP07(2024)257)] (2023)
* [Gradient Boosting MUST taggers for highly-boosted jets](https://arxiv.org/abs/2305.04957) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05781-0)] (2023)
* [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)] (2022)
* [A $W^\pm$ polarization analyzer from Deep Neural Networks](https://arxiv.org/abs/2102.05124) (2021)
* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)] (2020)
* [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)] (2020)
* [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)] (2019)
* [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)] (2020)
* [QCD-Aware Recursive Neural Networks for Jet Physics](https://arxiv.org/abs/1702.00748) [[DOI](https://doi.org/10.1007/JHEP01(2019)057)] (2017)
* [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)] (2016)
* [Jet-images — deep learning edition](https://arxiv.org/abs/1511.05190) [[DOI](https://doi.org/10.1007/JHEP07(2016)069)] (2015)
#### $H\rightarrow b\bar{b}$
* [Deep Learning to Improve the Sensitivity of Higgs Pair Searches in the $4b$ Channel at the LHC](https://arxiv.org/abs/2505.04496) (2025)
* [Application of Particle Transformer to quark flavor tagging in the ILC project](https://arxiv.org/abs/2410.11322) [[DOI](https://doi.org/10.1051/epjconf/202431503011)] (2024)
* [Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC](https://arxiv.org/abs/2410.04465) [[DOI](https://doi.org/10.1088/1674-1137/adacc5)] (2024)
* [Higgs tagging with the Lund jet plane](https://arxiv.org/abs/2105.03989) [[DOI](https://doi.org/10.1103/PhysRevD.104.055043)] (2021)
* [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)] (2021)
* [Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks](https://arxiv.org/abs/2010.08201) (2020)
* [The Boosted Higgs Jet Reconstruction via Graph Neural Network](https://arxiv.org/abs/2010.05464) [[DOI](https://doi.org/10.1103/PhysRevD.103.116025)] (2020)
* [Benchmarking Machine Learning Techniques with Di-Higgs Production at the LHC](https://arxiv.org/abs/2009.06754) (2020)
* [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)] (2020)
* [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)] (2020)
* [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)] (2019)
* [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)] (2019)
* [Boosting $H\to b\bar b$ with Machine Learning](https://arxiv.org/abs/1807.10768) [[DOI](https://doi.org/10.1007/JHEP10(2018)101)] (2018)
* [Automating the Construction of Jet Observables with Machine Learning](https://arxiv.org/abs/1902.07180) [[DOI](https://doi.org/10.1103/PhysRevD.100.095016)] (2019)
#### quarks and gluons
* [A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps](https://arxiv.org/abs/2505.13063) (2025)
* [The Fundamental Limit of Jet Tagging](https://arxiv.org/abs/2411.02628) (2024)
* [A Lorentz-Equivariant Transformer for All of the LHC](https://arxiv.org/abs/2411.00446) (2024)
* [Application of Particle Transformer to quark flavor tagging in the ILC project](https://arxiv.org/abs/2410.11322) [[DOI](https://doi.org/10.1051/epjconf/202431503011)] (2024)
* [Jet Tagging with More-Interaction Particle Transformer](https://arxiv.org/abs/2407.08682) [[DOI](https://doi.org/10.1088/1674-1137/ad7f3d)] (2024)
* [A multicategory jet image classification framework using deep neural network](https://arxiv.org/abs/2407.03524) (2024)
* [Jet Flavour Tagging at FCC-ee with a Transformer-based Neural Network: DeepJetTransformer](https://arxiv.org/abs/2406.08590) [[DOI](https://doi.org/10.1140/epjc/s10052-025-13785-y)] (2024)
* [Quark-versus-gluon tagging in CMS Open Data with CWoLa and TopicFlow](https://arxiv.org/abs/2312.03434) (2023)
* [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300) [[DOI](https://doi.org/10.1007/JHEP09(2023)135)] (2023)
* [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)] (2023)
* [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979) [[DOI](https://doi.org/10.1007/JHEP07(2024)257)] (2023)
* [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)] (2023)
* [Systematic Quark/Gluon Identification with Ratios of Likelihoods](https://arxiv.org/abs/2207.12411) [[DOI](https://doi.org/10.1007/JHEP12(2022)021)] (2022)
* [Quarks and gluons in the Lund plane](https://arxiv.org/abs/2112.09140) [[DOI](https://doi.org/10.1007/JHEP08(2022)177)] (2021)
* [Identifying the Quantum Properties of Hadronic Resonances using Machine Learning](https://arxiv.org/abs/2105.04582) [[DOI](https://doi.org/10.21468/SciPostPhysCore.8.2.039)] (2021)
* [Safety of Quark/Gluon Jet Classification](https://arxiv.org/abs/2103.09103) (2021)
* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)] (2020)
* [Quark-Gluon Jet Discrimination Using Convolutional Neural Networks](https://arxiv.org/abs/2012.02531) [[DOI](https://doi.org/10.3938/jkps.74.219)] (2020)
* [Quark Gluon Jet Discrimination with Weakly Supervised Learning](https://arxiv.org/abs/2012.02540) [[DOI](https://doi.org/10.3938/jkps.75.652)] (2020)
* [Towards Machine Learning Analytics for Jet Substructure](https://arxiv.org/abs/2007.04319) [[DOI](https://doi.org/10.1007/JHEP09(2020)195)] (2020)
* [Quark-Gluon Tagging: Machine Learning vs Detector](https://arxiv.org/abs/1812.09223) [[DOI](https://doi.org/10.21468/SciPostPhys.6.6.069)] (2018)
* [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)] (2019)
* [Probing heavy ion collisions using quark and gluon jet substructure](https://arxiv.org/abs/1803.03589) (2018)
* [DeepJet: Generic physics object based jet multiclass classification for LHC experiments](https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf) (2017)
* [Recursive Neural Networks in Quark/Gluon Tagging](https://arxiv.org/abs/1711.02633) [[DOI](https://doi.org/10.1007/s41781-018-0007-y)] (2017)
* [Deep learning in color: towards automated quark/gluon](https://arxiv.org/abs/1612.01551) [[DOI](https://doi.org/10.1007/JHEP01(2017)110)] (2016)
* [Quark versus Gluon Jet Tagging Using Jet Images with the ATLAS Detector](http://cds.cern.ch/record/2275641) (2017)
#### top quark tagging
* [Jet Reconstruction with Mamba Networks in Collider Events](https://arxiv.org/abs/2506.18336) (2025)
* [Transforming jet flavour tagging at ATLAS](https://arxiv.org/abs/2505.19689) (2025)
* [IAFormer: Interaction-Aware Transformer network for collider data analysis](https://arxiv.org/abs/2505.03258) (2025)
* [Product Manifold Machine Learning for Physics](https://arxiv.org/abs/2412.07033) (2024)
* [A Lorentz-Equivariant Transformer for All of the LHC](https://arxiv.org/abs/2411.00446) (2024)
* [Systematic Interpretability and the Likelihood for Boosted Top Quark Identification](https://arxiv.org/abs/2411.00104) (2024)
* [Application of Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM and BSM Processes](https://arxiv.org/abs/2410.13904) (2024)
* [Unveiling the Secrets of New Physics Through Top Quark Tagging](https://arxiv.org/abs/2409.12085) [[DOI](https://doi.org/10.1140/epjs/s11734-024-01257-5)] (2024)
* [Hadronic Top Quark Polarimetry with ParticleNet](https://arxiv.org/abs/2407.01663) [[DOI](https://doi.org/10.1016/j.physletb.2025.139314)] (2024)
* [The Phase Space Distance Between Collider Events](https://arxiv.org/abs/2405.16698) [[DOI](https://doi.org/10.1007/JHEP09(2024)054)] (2024)
* [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)] (2023)
* [Jet Classification Using High-Level Features from Anatomy of Top Jets](https://arxiv.org/abs/2312.11760) [[DOI](https://doi.org/10.1007/JHEP07(2024)146)] (2023)
* [Scaling Laws in Jet Classification](https://arxiv.org/abs/2312.02264) [[DOI](https://doi.org/10.21468/SciPostPhysCore.8.1.034)] (2023)
* [Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation](https://arxiv.org/abs/2311.14160) (2023)
* [19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics](https://arxiv.org/abs/2310.16121) (2023)
* [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) (2023)
* [ML-Based Top Taggers: Performance, Uncertainty and Impact of Tower \& Tracker Data Integration](https://arxiv.org/abs/2309.01568) [[DOI](https://doi.org/10.21468/SciPostPhys.17.6.166)] (2023)
* [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)] (2023)
* [Hierarchical High-Point Energy Flow Network for Jet Tagging](https://arxiv.org/abs/2308.08300) [[DOI](https://doi.org/10.1007/JHEP09(2023)135)] (2023)
* [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://arxiv.org/abs/2307.16506) [[DOI](https://doi.org/10.1007/JHEP03(2024)113)] (2023)
* [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)] (2023)
* [Machine Learning in Top Physics in the ATLAS and CMS Collaborations](https://arxiv.org/abs/2301.09534) (2023)
* [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)] (2023)
* [Boosted top tagging and its interpretation using Shapley values](https://arxiv.org/abs/2212.11606) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05910-9)] (2022)
* [BIP: Boost Invariant Polynomials for Efficient Jet Tagging](https://arxiv.org/abs/2207.08272) [[DOI](https://doi.org/10.1088/2632-2153/aca9ca)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2021)
* [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)] (2021)
* [Jet tagging in the Lund plane with graph networks](https://arxiv.org/abs/2012.08526) [[DOI](https://doi.org/10.1007/JHEP03(2021)052)] (2020)
* [Morphology for Jet Classification](https://arxiv.org/abs/2010.13469) [[DOI](https://doi.org/10.1103/PhysRevD.105.014004)] (2020)
* [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)] (2020)
* [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)] (2018)
* [Deep-learning Top Taggers or The End of QCD?](https://arxiv.org/abs/1701.08784) [[DOI](https://doi.org/10.1007/JHEP05(2017)006)] (2017)
* [Deep-learned Top Tagging with a Lorentz Layer](https://arxiv.org/abs/1707.08966) [[DOI](https://doi.org/10.21468/SciPostPhys.5.3.028)] (2017)
* [CapsNets Continuing the Convolutional Quest](https://arxiv.org/abs/1906.11265) [[DOI](https://doi.org/10.21468/SciPostPhys.8.2.023)] (2019)
* [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)] (2020)
* [The Machine Learning Landscape of Top Taggers](https://arxiv.org/abs/1902.09914) [[DOI](https://doi.org/10.21468/SciPostPhys.7.1.014)] (2019)
* [DeepJet: Generic physics object based jet multiclass classification for LHC experiments](https://dl4physicalsciences.github.io/files/nips_dlps_2017_10.pdf) (2017)
* [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)] (2015)
#### strange jets
* [A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps](https://arxiv.org/abs/2505.13063) (2025)
* [Improving the Direct Determination of $|V_{ts}|$ using Deep Learning](https://arxiv.org/abs/2502.02918) (2025)
* [Run 3 performance and advances in heavy-flavor jet tagging in CMS](https://arxiv.org/abs/2412.05863) [[DOI](https://doi.org/10.22323/1.476.0992)] (2024)
* [New Physics Through Flavor Tagging at FCC-ee](https://arxiv.org/abs/2411.02485) [[DOI](https://doi.org/10.21468/SciPostPhys.18.5.152)] (2024)
* [Application of Particle Transformer to quark flavor tagging in the ILC project](https://arxiv.org/abs/2410.11322) [[DOI](https://doi.org/10.1051/epjconf/202431503011)] (2024)
* [From strange-quark tagging to fragmentation tagging with machine learning](https://arxiv.org/abs/2408.12377) [[DOI](https://doi.org/10.1103/PhysRevD.111.034003)] (2024)
* [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)] (2023)
* [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)] (2020)
* [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)] (2019)
* [Strange Jet Tagging](https://arxiv.org/abs/2003.09517) (2020)
#### $b$-tagging
* [Transforming jet flavour tagging at ATLAS](https://arxiv.org/abs/2505.19689) (2025)
* [A continuous calibration of the ATLAS flavour-tagging classifiers via optimal transportation maps](https://arxiv.org/abs/2505.13063) (2025)
* [Machine Learning-Based b-Jet Tagging in pp Collisions at $\sqrt{s}](https://arxiv.org/abs/2504.18291) (2025)
* [Graph Neural Network Flavor Tagger and measurement of $\mathrm{sin}2\beta$ at Belle II](https://arxiv.org/abs/2501.17631) (2025)
* [Exploring jets: substructure and flavour tagging in CMS and ATLAS](https://arxiv.org/abs/2410.14330) [[DOI](https://doi.org/10.22323/1.478.0150)] (2024)
* [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) (2024)
* [Vertex Reconstruction with MaskFormers](https://arxiv.org/abs/2312.12272) [[DOI](https://doi.org/10.1140/epjc/s10052-024-13374-5)] (2023)
* [Neural networks for boosted di-$\tau$ identification](https://arxiv.org/abs/2312.08276) [[DOI](https://doi.org/10.1088/1748-0221/19/07/P07004)] (2023)
* [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)] (2023)
* [Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface](https://arxiv.org/abs/2303.14511) (2023)
* [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)] (2022)
* [Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS](https://cds.cern.ch/record/2718948) (2020)
* [Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment](http://cdsweb.cern.ch/record/2255226) (2017)
* [Jet Flavour Classification Using DeepJet](https://arxiv.org/abs/2008.10519) [[DOI](https://doi.org/10.1088/1748-0221/15/12/P12012)] (2020)
* [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)] (2020)
* [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)] (2018)
* [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)] (2016)
* [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)] (2017)
#### Flavor physics
* [Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks](https://arxiv.org/abs/2504.21844) (2025)
* [Charm-hadron reconstruction through three body decay in hadronic collisions using Machine Learning](https://arxiv.org/abs/2504.18279) (2025)
* [Graph Neural Network Flavor Tagger and measurement of $\mathrm{sin}2\beta$ at Belle II](https://arxiv.org/abs/2501.17631) (2025)
* [Reinforcement learning-based statistical search strategy for an axion model from flavor](https://arxiv.org/abs/2409.10023) (2024)
* [Holographic complex potential of a quarkonium from deep learning](https://arxiv.org/abs/2406.06285) (2024)
* [Exploring Transport Properties of Quark-Gluon Plasma with a Machine-Learning assisted Holographic Approach](https://arxiv.org/abs/2404.18217) [[DOI](https://doi.org/10.1103/PhysRevD.111.086033)] (2024)
* [Meson mass and width: Deep learning approach](https://arxiv.org/abs/2404.00448) [[DOI](https://doi.org/10.1103/PhysRevD.110.054011)] (2024)
* [A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements](https://arxiv.org/abs/2403.18265) [[DOI](https://doi.org/10.1103/PhysRevD.110.114034)] (2024)
* [Heavy quarkonium spectral function in an anisotropic background](https://arxiv.org/abs/2403.04966) [[DOI](https://doi.org/10.1103/PhysRevD.109.086010)] (2024)
* [Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN](https://arxiv.org/abs/2402.16493) [[DOI](https://doi.org/10.1007/s41365-025-01670-y)] (2024)
* [Differentiable Vertex Fitting for Jet Flavour Tagging](https://arxiv.org/abs/2310.12804) [[DOI](https://doi.org/10.1103/PhysRevD.110.052010)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Predicting Exotic Hadron Masses with Data Augmentation Using Multilayer Perceptron](https://arxiv.org/abs/2208.09538) [[DOI](https://doi.org/10.1142/S0217751X23500033)] (2022)
* ['Deep' Dive into $b \to c$ Anomalies: Standardized and Future-proof Model Selection Using Self-normalizing Neural Networks](https://arxiv.org/abs/2008.04316) (2020)
#### BSM particles and models
* [Testing a 95 GeV Scalar at the CEPC with Machine Learning](https://arxiv.org/abs/2506.21454) (2025)
* [Hunting and identifying coloured resonances in four top events with machine learning](https://arxiv.org/abs/2506.04318) (2025)
* [Tagging fully hadronic exotic decays of the vectorlike $\mathbf{B}$ quark using a graph neural network](https://arxiv.org/abs/2505.07769) (2025)
* [Heavy neutrino mixing prospects at hadron colliders: a machine learning study](https://arxiv.org/abs/2504.12141) (2025)
* [Constraining the 3HDM Parameter Space](https://arxiv.org/abs/2504.07489) (2025)
* [Mass-unspecific classifiers for mass-dependent searches](https://arxiv.org/abs/2503.20926) (2025)
* [Machine Learning Approaches to Top Quark Flavor-Changing Four-Fermion Interactions in Trilepton Signals at the LHC](https://arxiv.org/abs/2502.18667) [[DOI](https://doi.org/10.1103/mk8x-nrpn)] (2025)
* [Identification of tqg flavor-changing neutral current interactions using machine learning techniques](https://arxiv.org/abs/2502.04844) [[DOI](https://doi.org/10.1007/s40042-024-01277-3)] (2025)
* [Separation of left-handed and anomalous right-handed vector operators contributions into the Wtb vertex for single and double resonant top quark production processes using a neural network](https://arxiv.org/abs/2412.02468) [[DOI](https://doi.org/10.1134/S1063779624701776)] (2024)
* [Pseudo-observables and Deep Neural Network for mixed CP -- H to tau tau decays at LHC](https://arxiv.org/abs/2411.06216) (2024)
* [Improving smuon searches with Neural Networks](https://arxiv.org/abs/2411.04526) [[DOI](https://doi.org/10.1140/epjc/s10052-025-13748-3)] (2024)
* [Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC](https://arxiv.org/abs/2410.13799) [[DOI](https://doi.org/10.1007/JHEP07(2025)014)] (2024)
* [Machine learning tagged boosted dark photon: A signature of fermionic portal matter at the LHC](https://arxiv.org/abs/2410.06925) (2024)
* [Multiple testing for signal-agnostic searches of new physics with machine learning](https://arxiv.org/abs/2408.12296) [[DOI](https://doi.org/10.1140/epjc/s10052-024-13722-5)] (2024)
* [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203) [[DOI](https://doi.org/10.1103/PhysRevD.111.035007)] (2024)
* [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096) [[DOI](https://doi.org/10.1103/PhysRevD.110.056006)] (2024)
* [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499) (2024)
* [Leptoquark Searches at TeV Scale Using Neural Networks at Hadron Collider](https://arxiv.org/abs/2405.08090) (2024)
* [Reconstruction of Short-Lived Particles using Graph-Hypergraph Representation Learning](https://arxiv.org/abs/2402.10149) [[DOI](https://doi.org/10.1103/PhysRevD.111.032004)] (2024)
* [Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the $4b$ Channel](https://arxiv.org/abs/2401.14198) [[DOI](https://doi.org/10.1007/JHEP09(2024)139)] (2024)
* [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)] (2024)
* [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) (2024)
* [Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques](https://arxiv.org/abs/2401.05094) (2024)
* [Multi-scale cross-attention transformer encoder for event classification](https://arxiv.org/abs/2401.00452) [[DOI](https://doi.org/10.1007/JHEP03(2024)144)] (2024)
* [Quantum Metric Learning for New Physics Searches at the LHC](https://arxiv.org/abs/2311.16866) (2023)
* [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) [[DOI](https://doi.org/10.1140/epjc/s10052-024-13208-4)] (2023)
* [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)] (2023)
* [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)] (2023)
* [LLPNet: Graph Autoencoder for Triggering Light Long-Lived Particles at HL-LHC](https://arxiv.org/abs/2308.13611) (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Gradient Boosting MUST taggers for highly-boosted jets](https://arxiv.org/abs/2305.04957) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05781-0)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Search for vector-like leptons at a Muon Collider](https://arxiv.org/abs/2304.01885) [[DOI](https://doi.org/10.1088/1674-1137/ace5a7)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Optimal Mass Variables for Semivisible Jets](https://arxiv.org/abs/2303.16253) [[DOI](https://doi.org/10.21468/SciPostPhysCore.6.4.067)] (2023)
* [Probing Heavy Neutrinos at the LHC from Fat-jet using Machine Learning](https://arxiv.org/abs/2303.15920) (2023)
* [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) [[DOI](https://doi.org/10.1088/1361-6471/ad4fab)] (2023)
* [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)] (2023)
* [Search for Electroweak Production of Supersymmetric Particles in Compressed Mass Spectra With the ATLAS Detector at the LHC](https://arxiv.org/abs/2211.11642) (2022)
* [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)] (2022)
* [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)] (2022)
* [Searching for exotic Higgs bosons from top quark decays at the HL-LHC](https://arxiv.org/abs/2212.09061) [[DOI](https://doi.org/10.1140/epjc/s10052-024-13274-8)] (2022)
* [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)] (2022)
* [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)] (2022)
* [Learning to Identify Semi-Visible Jets](https://arxiv.org/abs/2208.10062) [[DOI](https://doi.org/10.1007/JHEP12(2022)132)] (2022)
* [Machine-enhanced CP-asymmetries in the electroweak sector](https://arxiv.org/abs/2209.05143) [[DOI](https://doi.org/10.1103/PhysRevD.107.016008)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [Active learning BSM parameter spaces](https://arxiv.org/abs/2204.13950) [[DOI](https://doi.org/10.1140/epjc/s10052-023-11368-3)] (2022)
* [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)] (2022)
* [Solving Combinatorial Problems at Particle Colliders Using Machine Learning](https://arxiv.org/abs/2201.02205) [[DOI](https://doi.org/10.1103/PhysRevD.106.016001)] (2022)
* [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)] (2022)
* [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)] (2021)
* [Event-level variables for semivisible jets using anomalous jet tagging](https://arxiv.org/abs/2111.12156) (2021)
* [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)] (2021)
* [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)] (2021)
* [Machine Learning Optimized Search for the $Z'$ from $U(1)_{L_\mu-L_\tau}$ at the LHC](https://arxiv.org/abs/2109.07674) (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [Unsupervised Hadronic SUEP at the LHC](https://arxiv.org/abs/2107.12379) [[DOI](https://doi.org/10.1007/JHEP12(2021)129)] (2021)
* [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)] (2021)
* [Top squark signal significance enhancement by different Machine Learning Algorithms](https://arxiv.org/abs/2106.06813) [[DOI](https://doi.org/10.1142/S0217751X22501974)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2019)
* [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)] (2019)
* [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)] (2020)
* [Sensing Higgs cascade decays through memory](https://arxiv.org/abs/2008.08611) [[DOI](https://doi.org/10.1103/PhysRevD.102.095027)] (2020)
* [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)] (2020)
* [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)] (2020)
* [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)] (2020)
* [Distinguishing $W'$ Signals at Hadron Colliders Using Neural Networks](https://arxiv.org/abs/2007.14586) [[DOI](https://doi.org/10.1103/PhysRevD.103.036016)] (2020)
* [Casting a graph net to catch dark showers](https://arxiv.org/abs/2006.08639) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.046)] (2020)
* [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)] (2020)
* [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)] (2019)
* [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)] (2019)
* [Searching for Exotic Particles in High-Energy Physics with Deep Learning](https://arxiv.org/abs/1402.4735) [[DOI](https://doi.org/10.1038/ncomms5308)] (2014)
* [Automating the Construction of Jet Observables with Machine Learning](https://arxiv.org/abs/1902.07180) [[DOI](https://doi.org/10.1103/PhysRevD.100.095016)] (2019)
#### Particle identification
* [ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3](https://arxiv.org/abs/2507.02817) (2025)
* [Jet Reconstruction with Mamba Networks in Collider Events](https://arxiv.org/abs/2506.18336) (2025)
* [Performance of the FARICH-based particle identification at charm superfactories using machine learning](https://arxiv.org/abs/2506.14247) (2025)
* [Particle identification in the GlueX detector using a multi-layer perceptron](https://arxiv.org/abs/2505.14706) (2025)
* [Deep Neural Networks for Cross-Energy Particle Identification at RHIC and LHC](https://arxiv.org/abs/2505.06732) (2025)
* [Charm-hadron reconstruction through three body decay in hadronic collisions using Machine Learning](https://arxiv.org/abs/2504.18279) (2025)
* [Measurement of the atmospheric $\nu_{\mu}$ flux with six detection units of KM3NeT/ORCA](https://arxiv.org/abs/2504.09119) (2025)
* [Reconstruction of muon bundles in KM3NeT detectors using machine learning methods](https://arxiv.org/abs/2503.01433) (2025)
* [Transformer networks for Heavy flavor jet tagging](https://arxiv.org/abs/2411.11519) [[DOI](https://doi.org/10.7566/JPSJ.94.031007)] (2024)
* [Transformers for Charged Particle Track Reconstruction in High Energy Physics](https://arxiv.org/abs/2411.07149) (2024)
* [Detecting highly collimated photon-jets from Higgs boson exotic decays with deep learning](https://arxiv.org/abs/2401.15690) (2024)
* [Machine-learning-based particle identification with missing data](https://arxiv.org/abs/2401.01905) [[DOI](https://doi.org/10.1140/epjc/s10052-024-13047-3)] (2024)
* [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)] (2023)
* [Particle identification with machine learning in ALICE Run 3](https://arxiv.org/abs/2309.07768) [[DOI](https://doi.org/10.1051/epjconf/202429509029)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [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)] (2023)
* [Separation of electrons from pions in GEM TRD using deep learning](https://arxiv.org/abs/2303.10776) (2023)
* [Robust Neural Particle Identification Models](https://arxiv.org/abs/2212.07274) [[DOI](https://doi.org/10.1088/1742-6596/2438/1/012119)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2022)
* [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)] (2021)
* [Shower Identification in Calorimeter using Deep Learning](https://arxiv.org/abs/2103.16247) (2021)
* [Learning to Identify Electrons](https://arxiv.org/abs/2011.01984) [[DOI](https://doi.org/10.1103/PhysRevD.103.116028)] (2020)
* [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)] (2019)
* [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)] (2019)
* [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)] (2018)
* [Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics](https://dl4physicalsciences.github.io/files/nips_dlps_2017_15.pdf) (2017)
* [Survey of Machine Learning Techniques for High Energy Electromagnetic Shower Classification](https://dl4physicalsciences.github.io/files/nips_dlps_2017_24.pdf) (2017)
* [Electromagnetic Showers Beyond Shower Shapes](https://arxiv.org/abs/1806.05667) [[DOI](https://doi.org/10.1016/j.nima.2019.162879)] (2018)
#### Neutrino Detectors
* [Neutrino Telescope Event Classification on Quantum Computers](https://arxiv.org/abs/2506.16530) (2025)
* [Hybrid-Graph Neural Network Method for Muon Fast Reconstruction in Neutrino Telescopes](https://arxiv.org/abs/2505.23425) (2025)
* [Measurement of the atmospheric $\nu_{\mu}$ flux with six detection units of KM3NeT/ORCA](https://arxiv.org/abs/2504.09119) (2025)
* [Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2504.08182) (2025)
* [LArTPC hit-based topology classification with quantum machine learning and symmetry](https://arxiv.org/abs/2503.12655) (2025)
* [Reconstruction of muon bundles in KM3NeT detectors using machine learning methods](https://arxiv.org/abs/2503.01433) (2025)
* [Contrastive Learning for Robust Representations of Neutrino Data](https://arxiv.org/abs/2502.07724) [[DOI](https://doi.org/10.1103/PhysRevD.111.092011)] (2025)
* [Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning](https://arxiv.org/abs/2502.06637) [[DOI](https://doi.org/10.1140/epjc/s10052-025-14313-8)] (2025)
* [Machine Learning Neutrino-Nucleus Cross Sections](https://arxiv.org/abs/2412.16303) (2024)
* [Anomalous electroweak physics unraveled via evidential deep learning](https://arxiv.org/abs/2412.16286) (2024)
* [Machine Learning-Powered Data Cleaning for LEGEND](https://arxiv.org/abs/2410.14701) [[DOI](https://doi.org/10.1088/2632-2153/adbb37)] (2024)
* [Learning Efficient Representations of Neutrino Telescope Events](https://arxiv.org/abs/2410.13148) (2024)
* [Real-time Position Reconstruction for the KamLAND-Zen Experiment using Hardware-AI Co-design](https://arxiv.org/abs/2410.02991) (2024)
* [Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution](https://arxiv.org/abs/2408.08474) [[DOI](https://doi.org/10.1103/PhysRevD.111.L041301)] (2024)
* [Improving Neutrino Energy Reconstruction with Machine Learning](https://arxiv.org/abs/2405.15867) (2024)
* [RELICS: a REactor neutrino LIquid xenon Coherent elastic Scattering experiment](https://arxiv.org/abs/2405.05554) [[DOI](https://doi.org/10.1103/PhysRevD.110.072011)] (2024)
* [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) [[DOI](https://doi.org/10.1103/PhysRevLett.134.091801)] (2024)
* [NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction](https://arxiv.org/abs/2403.11872) [[DOI](https://doi.org/10.1103/PhysRevD.110.032008)] (2024)
* [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)] (2024)
* [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)] (2024)
* [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)] (2023)
* [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05287-9)] (2023)
* [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)] (2022)
* [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)] (2022)
* [GraphNeT: Graph neural networks for neutrino telescope event reconstruction](https://arxiv.org/abs/2210.12194) [[DOI](https://doi.org/10.21105/joss.04971)] (2022)
* [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)] (2022)
* [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)] (2022)
* [Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers](https://arxiv.org/abs/2204.02496) (2022)
* [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)] (2022)
* [Improvement of the NOvA Near Detector Event Reconstruction and Primary Vertexing through the Application of Machine Learning Methods](https://arxiv.org/abs/2112.01494) (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [Deep learning reconstruction in ANTARES](https://arxiv.org/abs/2107.13654) [[DOI](https://doi.org/10.1088/1748-0221/16/09/C09018)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2021)
* [Deep Learning strategies for ProtoDUNE raw data denoising](https://arxiv.org/abs/2103.01596) [[DOI](https://doi.org/10.1007/s41781-021-00077-9)] (2021)
* [Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors](https://arxiv.org/abs/2102.01033) (2021)
* [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)] (2021)
* [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)] (2021)
* [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)] (2020)
* [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)] (2020)
* [Deep-Learning-Based Kinematic Reconstruction for DUNE](https://arxiv.org/abs/2012.06181) (2020)
* [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)] (2020)
* [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)] (2020)
* [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)] (2020)
* [A Review on Machine Learning for Neutrino Experiments](https://arxiv.org/abs/2008.01242) [[DOI](https://doi.org/10.1142/S0217751X20430058)] (2020)
* [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)] (2020)
* [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) (2020)
* [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)] (2020)
* [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)] (2020)
* [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)] (2020)
* [PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics](https://arxiv.org/abs/2006.01993) (2020)
* [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)] (2020)
* [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)] (2019)
* [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)] (2018)
* [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) (2017)
* [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)] (2016)
* [A Convolutional Neural Network Neutrino Event Classifier](https://arxiv.org/abs/1604.01444) [[DOI](https://doi.org/10.1088/1748-0221/11/09/P09001)] (2016)
#### Direct Dark Matter Detectors
* [Bayesian network 3D event reconstruction in the Cygno optical TPC for dark matter direct detection](https://arxiv.org/abs/2506.04973) (2025)
* [Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection](https://arxiv.org/abs/2407.21008) [[DOI](https://doi.org/10.1088/1475-7516/2025/01/038)] (2024)
* [Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors](https://arxiv.org/abs/2403.15949) [[DOI](https://doi.org/10.1088/2632-2153/ad5f13)] (2024)
* [Detector signal characterization with a Bayesian network in XENONnT](https://arxiv.org/abs/2304.05428) [[DOI](https://doi.org/10.1103/PhysRevD.108.012016)] (2023)
* [Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors](https://arxiv.org/abs/2305.09744) [[DOI](https://doi.org/10.1140/epjp/s13360-024-05287-9)] (2023)
* [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)] (2022)
* [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)] (2021)
* [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)] (2021)
* [Machine-learning techniques applied to three-year exposure of ANAIS-112](https://arxiv.org/abs/2110.10