| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 09043 | |
| Number of page(s) | 5 | |
| Section | Artificial Intelligence and Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202429509043 | |
| Published online | 06 May 2024 | |
https://doi.org/10.1051/epjconf/202429509043
What Machine Learning Can Do for Focusing Aerogel Detectors
1 NRU Higher School of Economics, Moscow, Russia
2 Budker Institute of Nuclear Physics of Siberian Branch Russian Academy of Sciences, Novosibirsk, Russia
3 Novosibirsk State Technical University, Novosibirsk, Russia
4 Novosibirsk State University, Novosibirsk, Russia
* e-mail: foma@shipilov.ru
Published online: 6 May 2024
Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARICH). The specifics of detector location make proper cooling difficult, therefore a significant number of ambient background hits are captured. They must be mitigated to reduce the data flow and improve particle velocity resolution. In this work we present several approaches to filtering signal hits, inspired by machine learning techniques from computer vision.
© The Authors, published by EDP Sciences, 2024
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