Deep learning techniques for energy clustering in the CMS electromagnetic calorimeter

Abstract

The reconstruction of electrons and photons in CMS depends on the topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). The currently used algorithm cannot account for the energy deposits coming from the pileup (secondary collisions) efficiently. The performance of this algorithm is expected to degrade during the LHC Run 3 because of the larger average pileup level and the increasing level of noise due to the aging of the ECAL detector. In this paper, we explore new techniques for energy reconstruction in ECAL using state-of-the-art machine learning algorithms like graph neural networks and self-attention modules.