Speaker
Description
This work investigates energy deposition within the scintillating tiles of the Barrel Hadronic Calorimeter (BHCal) of the ePIC detector at the Electron-Ion Collider (EIC). The BHCal is central in calibrating jet energy scales, measuring hadronic final states, tagging charged-current Deep Inelastic Scattering (DIS) events, and identifying muons. Using simulation studies, we analyze how particles deposit energy into the scintillator material, which is detected using silicon photomultipliers (SiPMs). Particular emphasis is given to the reconstruction of muons, leveraging their deep penetration and characteristic low-energy deposition profiles. Machine learning techniques improve muon identification by examining energy deposition patterns. This research advances efforts to optimize BHCal performance for precise measurements in support of the broader EIC physics program.
