Artificial Intelligence for Imaging Cherenkov Detectors at the EIC

TL;DR

This paper explores the application of artificial intelligence to enhance the design, simulation, and pattern recognition capabilities of imaging Cherenkov detectors at the Electron Ion Collider, aiming to improve particle identification.

Contribution

It presents novel AI-based methods for designing and simulating Cherenkov detectors and for pattern recognition in particle identification at EIC.

Findings

AI-assisted design improves detector optimization.

AI enhances simulation accuracy for complex optical processes.

AI-based pattern recognition aids in particle identification.

Abstract

Imaging Cherenkov detectors form the backbone of particle identification (PID) at the future Electron Ion Collider (EIC). Currently all the designs for the first EIC detector proposal use a dual Ring Imaging CHerenkov (dRICH) detector in the hadron endcap, a Detector for Internally Reflected Cherenkov (DIRC) light in the barrel, and a modular RICH (mRICH) in the electron endcap. These detectors involve optical processes with many photons that need to be tracked through complex surfaces at the simulation level, while for reconstruction they rely on pattern recognition of ring images. This proceeding summarizes ongoing efforts and possible applications of AI for imaging Cherenkov detectors at EIC. In particular we will provide the example of the dRICH for the AI-assisted design and of the DIRC for simulation and particle identification from complex patterns and discuss possible advantages of using AI.