Speaker
Description
In the context of a gas-sampling Digital Hadronic Calorimeter (DHCAL), we explore the potential of using Graph Neural Networks (GNNs) for hadron energy reconstruction and Particle Identification (PID) in future collider experiments. GEANT4 was used to model a DHCAL module and generate datasets for training and testing the performance of the GNNs. 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.