Speaker
Description
This work focuses on the behavior of energy deposition within scintillating tiles of the Barrel Hadronic Calorimeter (BHCal) of the ePIC detector at the Electron-Ion Collider (EIC). The BHCal is integral to calibrating jet energy scales, measurements of hadronic final states, tagging of charged-current DIS events, and identification of muons.
Through 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, capitalizing on their capacity for deep penetration and characteristic low-energy deposition through machine learning techniques, used to improve muon identification by examining energy deposition patterns.
This endeavor contributes to the overall objectives of optimizing BHCal performance for precise measurements within the EIC physics program.
