# Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter

arXiv: https://arxiv.org/abs/2610.04058 · HTML: https://mlsanity.com/paper/2610.04058/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 3 of 3)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)
- [Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC](https://mlsanity.com/paper/2303.15291/) (2023)

## Abstract (arXiv)

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.