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10.3390/particles8020058
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Article

Hadron Identification Prospects with Granular Calorimeters

by
Andrea De Vita
1,3,*,
Abhishek
4,
Max Aehle
5,†,
Muhammad Awais
1,3,†,
Alessandro Breccia
1,
Riccardo Carroccio
1,3,
Long Chen
5,†,
Tommaso Dorigo
2,3,†,‡,
Nicolas R. Gauger
5,†,
Ralf Keidel
5,†,
Jan Kieseler
6,†,
Enrico Lupi
1,3,
Federico Nardi
1,7,†,
Xuan Tung Nguyen
3,5,
Fredrik Sandin
2,†,
Kylian Schmidt
6,
Pietro Vischia
8,† and
Joseph Willmore
3
1
Dipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, Italy
2
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
3
INFN, Sezione di Padova, Via F. Marzolo 8, 35131 Padova, Italy
4
National Institute of Science Education and Research, Jatni 752050, India
5
Chair for Scientific Computing, University of Kaiserslautern-Landau (RPTU), Paul-Ehrlich-Straße, 67663 Kaiserslautern, Germany
6
Institute for Experimental Particle Physics, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
7
Laboratoire de Physique Clermont Auvergne, 63170 Aubière, France
8
Department of Physics, Universidad de Oviedo and ICTEA, 33004 Oviedo, Spain
*
Author to whom correspondence should be addressed.
†
MODE Collaboration: https://mode-collaboration.github.io.
‡
Universal Scientific Education and Research Network, Italy.
Particles 2025, 8(2), 58; https://doi.org/10.3390/particles8020058
Submission received: 1 February 2025 / Revised: 2 April 2025 / Accepted: 19 April 2025 / Published: 16 May 2025
(This article belongs to the Special Issue Selected Papers from the 4th MODE Workshop on Differentiable Programming for Experiment Design)
Versions Notes

Abstract

In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy depositions in dense matter, along with the time of the interactions. Using GEANT4 simulations of a homogeneous lead tungstate calorimeter with high transverse and longitudinal segmentation, we investigated the discrimination of protons, positive pions, and positive kaons at 100 GeV. The analysis focuses on the impact of calorimeter granularity by progressively merging detector cells and extracting features like energy deposition patterns and timing information. Two machine learning approaches, XGBoost and fully connected deep neural networks, were employed to assess the classification performance across particle pairs. The results indicate that fine segmentation improves particle discrimination, with higher granularity yielding more detailed characterization of energy showers. Additionally, the results highlight the importance of shower radius, energy fractions, and timing variables in distinguishing particle types. The XGBoost model demonstrated computational efficiency and interpretability advantages over deep learning for tabular data structures, while achieving similar classification performance. This motivates further work required to combine high- and low-level feature analysis, e.g., using convolutional and graph-based neural networks, and extending the study to a broader range of particle energies and types.
Keywords: particle detectors; calorimetry; particle identification; physics; machine learning particle detectors; calorimetry; particle identification; physics; machine learning

Share and Cite

MDPI and ACS Style

De Vita, A.; Abhishek; Aehle, M.; Awais, M.; Breccia, A.; Carroccio, R.; Chen, L.; Dorigo, T.; Gauger, N.R.; Keidel, R.; et al. Hadron Identification Prospects with Granular Calorimeters. Particles 2025, 8, 58. https://doi.org/10.3390/particles8020058

AMA Style

De Vita A, Abhishek, Aehle M, Awais M, Breccia A, Carroccio R, Chen L, Dorigo T, Gauger NR, Keidel R, et al. Hadron Identification Prospects with Granular Calorimeters. Particles. 2025; 8(2):58. https://doi.org/10.3390/particles8020058

Chicago/Turabian Style

De Vita, Andrea, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, and et al. 2025. "Hadron Identification Prospects with Granular Calorimeters" Particles 8, no. 2: 58. https://doi.org/10.3390/particles8020058

APA Style

De Vita, A., Abhishek, Aehle, M., Awais, M., Breccia, A., Carroccio, R., Chen, L., Dorigo, T., Gauger, N. R., Keidel, R., Kieseler, J., Lupi, E., Nardi, F., Nguyen, X. T., Sandin, F., Schmidt, K., Vischia, P., & Willmore, J. (2025). Hadron Identification Prospects with Granular Calorimeters. Particles, 8(2), 58. https://doi.org/10.3390/particles8020058

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