Deep Learning Approaches for LHCb ECAL Reconstruction

Abstract

Calorimeters are a crucial component for most detectors mounted on modern colliders. Their tasks include identifying and measuring the energy of photons and neutral hadrons, recording energetic hadronic jets, and contributing to the identification of electrons, muons, and charged hadrons. To fulfill these many tasks while keeping costs reasonable, the calorimeter construction requires good and thoughtful balancing with other components of the detector. Much harder operation conditions during LHC’s high luminosity Run 5 and beyond bring new technological and computational challenges. This requires optimization of technologies, layouts, readouts, reconstruction algorithms to achieve the best overall physics performance for the limited cost. In the traditional approach, the reconstruction of the physical objects in the calorimeter must be matched to the calorimetric showers simulation used. We present a deep learning-based approach to help utilize raw simulated calorimetric data of varying degrees of detail.