Speaker
Description
We present the first application of a one-pass, machine learning based imaging calorimeter reconstruction approach to the latest full CMS High Granularity Calorimeter (HGCAL) simulation. The model is a Graph Neural Network that directly processes the hits in the HGCAL, one of the most important upgrades of the Compact Muon Solenoid detector in preparation for the High-Luminosity phase of the Large Hadron Collider planned to begin operations in 2030. The network is trained to group hits originating from the same incident particle by assigning them to a common cluster. The accuracy of the reconstruction is evaluated through physics-inspired metrics that quantify how accurately the properties of individual particles are measured. The algorithm is studied using simulations of different particle types in HGCAL and its performance is tested in single-particle environments.