Artificial Intelligence for Imaging Cherenkov Detectors at the EIC

• URL: http://arxiv.org/abs/2204.08645v1
• Date: Tue, 19 Apr 2022 03:58:01 GMT
• Title: Artificial Intelligence for Imaging Cherenkov Detectors at the EIC
• Authors: C. Fanelli and A. Mahmood
• Abstract summary: Imaging Cherenkov detectors form the backbone of particle identification (PID) at the future Electron Ion Collider (EIC) This proceeding summarizes ongoing efforts and possible applications of AI for imaging Cherenkov detectors at EIC.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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