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.

Instrumentation and Detectors (physics.ins-det); Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex)

arXiv:2610.04058 [physics.ins-det]

(or arXiv:2610.04058v1 [physics.ins-det] for this version)

https://doi.org/10.48550/arXiv.2610.04058 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1051/epjconf/202638603003 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.