Speaker
Description
The increasing importance of high-granularity calorimetry in particle physics origins from its ability to enhance event reconstruction and jet substructure analysis. In particular, the identification of hadronic decays within boosted jets and the application of particle flow techniques have demonstrated the advantages of fine spatial resolution in calorimeters. In this study, we investigate whether arbitrarily high granularity can also facilitate the classification of hadron-induced showers and aim to determine the granularity scale at which information on particle identity is extractable or lost. Using GEANT4, we simulate a 100 × 100 × 200 cells calorimeter composed of Lead Tungstate (PbWO₄), where each cell has dimensions of 3 mm × 3 mm × 6 mm. We analyse the discrimination of showers produced by protons, charged pions, and kaons based on the detailed topology of energy deposition. To achieve this, we used deep learning algorithms, specifically Deep Neural Networks, to classify the shower patterns and evaluate the impact of calorimeter granularity on discrimination power. Our preliminary results indicate significant potential for hadron identification through high-granularity calorimetry, which could improve particle identification in future high-energy physics experiments.
