Machine Learning for Imaging Cherenkov Detectors
This work targets researchers in nuclear and particle physics, but it appears incremental as it reviews recent advances without presenting new results.
The paper addresses the growing computing demands in nuclear and particle physics experiments by exploring AI-based approaches for imaging Cherenkov detectors, focusing on applications in detector design, calibration, and particle identification.
Imaging Cherenkov detectors are largely used in modern nuclear and particle physics experiments where cutting-edge solutions are needed to face always more growing computing demands. This is a fertile ground for AI-based approaches and at present we are witnessing the onset of new highly efficient and fast applications. This paper focuses on novel directions with applications to Cherenkov detectors. In particular, recent advances on detector design and calibration, as well as particle identification are presented.