Speaker
Description
Measurement of jets and their substructure will provide valuable information about the underlying dynamics of hard-scattered quarks and gluons in Deep-Inelastic Scattering events. The ePIC Barrel Hadronic Calorimeter (BHCal) will be a critical tool for such measurements at the Electron-Ion Collider. By enabling the measurement of the neutral hadronic component of jets, the BHCal will complement the Barrel Imaging Calorimeter (BIC) and the ePIC tracking system to improve our knowledge of the jet energy scale. However, to obtain a physically meaningful measurement, the response of the combined BIC + BHCal system must be properly calibrated using information from both. We present a potential Machine Learning (ML) based algorithm for the calibration of the combined system. With ML, this calibration can be done in such a way that is both computationally efficient and easy to deploy in a production environment, making such an approach ideal for quasi-real time calibrations needed in a streaming readout environment. We will discuss progress towards its implementation as well as the role it might play in a broader ML-based Particle Flow Algorithm.
