Artificial Intelligence for Imaging Cherenkov Detectors at the EIC
This work addresses particle identification problems for high-energy physics experiments at the EIC, but it is incremental as it summarizes ongoing efforts without presenting new results.
The paper explores applying AI to imaging Cherenkov detectors at the Electron Ion Collider (EIC) to tackle challenges in design, simulation, and particle identification from complex optical patterns, with examples including AI-assisted design for dRICH and pattern recognition for DIRC.
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.