Machine Learning for Imaging Cherenkov Detectors

TL;DR

This paper explores the application of machine learning techniques to improve the design, calibration, and particle identification processes in imaging Cherenkov detectors, addressing increasing computational challenges in physics experiments.

Contribution

It introduces novel AI-based methods specifically tailored for Cherenkov detector optimization, calibration, and particle identification, advancing current approaches in the field.

Findings

Enhanced detector calibration accuracy

Improved particle identification efficiency

Faster data processing methods

Abstract

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.