https://github.com/philippeller/freedom
Likelihood reconstruction using machine learning for arbitrary detector geometries
https://github.com/philippeller/freedom
Last synced: 2 months ago
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Likelihood reconstruction using machine learning for arbitrary detector geometries
- Host: GitHub
- URL: https://github.com/philippeller/freedom
- Owner: philippeller
- License: apache-2.0
- Created: 2020-05-06T09:22:47.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-24T13:12:57.000Z (over 2 years ago)
- Last Synced: 2025-04-09T17:14:30.528Z (2 months ago)
- Language: Jupyter Notebook
- Size: 86.2 MB
- Stars: 0
- Watchers: 6
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# *free*DOM
Likelihood reconstruction using machine learning for arbitrary detector geometries
This repository is used for the R&D of event recosntrcution in IceCube, based on approximate likelihoods.
Use the [wiki](https://github.com/philippeller/freeDOM/wiki) for things other than code!
## Installation
It is recommended to install in-place, i.e.:
```
pip install --editable .[full]
```
## Reference
This method was published as:
A flexible event reconstruction based on machine learning and likelihood principles
Philipp Eller (Munich, Tech. U.), Aaron T. Fienberg (Penn State U.), Jan Weldert (Mainz U., Inst. Phys.), Garrett Wendel (Penn State U.), Sebastian Böser (Mainz U., Inst. Phys.) et al.
e-Print: [2208.10166](https://arxiv.org/abs/2208.10166)
DOI: [10.1016/j.nima.2023.168011](https://doi.org/10.1016/j.nima.2023.168011) (publication)
Nucl.Instrum.Meth.A 1048 (2023), 168011
Please cite as
```
@article{Eller:2022xvi,
author = {Eller, Philipp and Fienberg, Aaron T. and Weldert, Jan and Wendel, Garrett and B\"oser, Sebastian and Cowen, D. F.},
title = "{A flexible event reconstruction based on machine learning and likelihood principles}",
eprint = "2208.10166",
archivePrefix = "arXiv",
primaryClass = "hep-ex",
doi = "10.1016/j.nima.2023.168011",
journal = "Nucl. Instrum. Meth. A",
volume = "1048",
pages = "168011",
year = "2023"
}
```