PreprintCC BY 4.0arXiv
Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
Abstract
In this paper we use machine learning methods to predict radiation damage in the lead tungstate crystals of the CMS electromagnetic calorimeter at the Large Hadron Collider. We analyze LHC Open Data collected from 2016 to 2018 and study the time evolution of the crystal optical transparency. We apply deep neural network models to predict its behavior over different future time intervals, and find that encoder-decoder sequence-to-sequence architectures can effectively describe crystal aging.
Versions
Related
- Kinematic Fitting of electromagnetic calorimeter data - an improved method
- Enhancing electromagnetic calorimeter signal reconstruction with machine learning-based noise discrimination
- Calibration of electromagnetic shower features in the CMS calorimeter with machine-learning techniques
- A Coherent Memory Register for Sequential Quantum Generative Modeling, with Application to Calorimeter Showers
- Signatures in the local interstellar medium of electromagnetic radiation produced by neutron star gravitational waves
BibTeX
@misc{torres2026application,
title = {{Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter}},
author = {Mario Ivan Gallegos Torres and Leonid Serkin and Guy Paic},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.04058},
eprint = {2610.04058},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.04058},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
AU - Mario Ivan Gallegos Torres
AU - Leonid Serkin
AU - Guy Paic
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.04058
UR - https://arxiv.org/abs/2610.04058
AB - In this paper we use machine learning methods to predict radiation damage in the lead tungstate crystals of the CMS electromagnetic calorimeter at the Large Hadron Collider. We analyze LHC Open Data collected from 2016 to 2018 and study the time evolution of the crystal optical transparency. We apply deep neural network models to predict its behavior over different future time intervals, and find that encoder-decoder sequence-to-sequence architectures can effectively describe crystal aging.
ER -