Speaker
Description
Measurement of jets and their substructure will provide valuable information about the properties of the struck quarks and their radiative properties in Deep-Inelastic Scattering events. The ePIC Barrel Hadronic Calorimeter (BHCal) will be a critical tool for such measurements. By enabling the measurement of the neutral hadronic component of jets, the BHCal will complement the Barrel Electromagnetic Calorimeter and improve our knowledge of the jet energy scale. However, to obtain a physically meaningful measurement, the response of the BHCal must be properly calibrated. In this presentation we provide a snapshot of ongoing studies exploring the use of Machine Learning to calibrate the response of the BHCal. Furthermore, we discuss ongoing efforts to characterize the performance of the BHCal in jet measurements while also providing a general overview of the detector and its design.
