Exploring DHCAL design and performance with Graph Neural Networks

Abstract

In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNN) for hadron energy reconstruction and Particle Identification (PID) in future collider experiments. For PID, we achieved classification efficiencies exceeding 50% for neutrons and pions, with notably higher efficiencies for kaons and protons. Protons exhibited the highest efficiency of 77%, followed by neutral kaons. The energy resolution for these hadrons is studied in the energy range of 1 -- 50 GeV, with a further investigation into the resolution as a function of the incoming particle's angle and readout granularity, focusing on charged pions. Compared to traditional analysis methods, our results indicate that improved performance can be achieved even with coarser detector granularity, potentially making future DHCAL systems more cost-effective.