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Particles, Volume 8, Issue 2
June 2025 - 28 articles
Cover Story: In this study, we explore the potential of granular calorimeters for identifying high-energy hadrons by analyzing energy deposition patterns and timing information. GEANT4 simulations of a highly segmented PbWO₄ calorimeter were utilized to assess the discrimination of protons, pions, and kaons at 100 GeV. By extracting detailed topological and temporal features, machine learning models such as XGBoost and deep neural networks were trained and evaluated. Our results demonstrate that increased segmentation enhances classification: p/π accuracy reaches ~62%, π/K reaches ~57%, and p/K reaches ~58.6% with XGBoost. Key discriminating variables are the shower radius and the total deposited energy. These findings provide insights for the design of future high-performance calorimeters. View this paper
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Particles - ISSN 2571-712XCreative Common CC BY license