Hadron Identification Prospects with Granular Calorimeters
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 longi...
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MDPI AG
2025-05-01
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| author | Andrea De Vita Abhishek Max Aehle Muhammad Awais Alessandro Breccia Riccardo Carroccio Long Chen Tommaso Dorigo Nicolas R. Gauger Ralf Keidel Jan Kieseler Enrico Lupi Federico Nardi Xuan Tung Nguyen Fredrik Sandin Kylian Schmidt Pietro Vischia Joseph Willmore |
| author_facet | Andrea De Vita Abhishek Max Aehle Muhammad Awais Alessandro Breccia Riccardo Carroccio Long Chen Tommaso Dorigo Nicolas R. Gauger Ralf Keidel Jan Kieseler Enrico Lupi Federico Nardi Xuan Tung Nguyen Fredrik Sandin Kylian Schmidt Pietro Vischia Joseph Willmore |
| author_sort | Andrea De Vita |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-70a2c6638dec49a9b6078a35ee0f023a |
| institution | OA Journals |
| issn | 2571-712X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Particles |
| spelling | doaj-art-70a2c6638dec49a9b6078a35ee0f023a2025-08-20T02:21:57ZengMDPI AGParticles2571-712X2025-05-01825810.3390/particles8020058Hadron Identification Prospects with Granular CalorimetersAndrea De Vita0Abhishek1Max Aehle2Muhammad Awais3Alessandro Breccia4Riccardo Carroccio5Long Chen6Tommaso Dorigo7Nicolas R. Gauger8Ralf Keidel9Jan Kieseler10Enrico Lupi11Federico Nardi12Xuan Tung Nguyen13Fredrik Sandin14Kylian Schmidt15Pietro Vischia16Joseph Willmore17Dipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, ItalyNational Institute of Science Education and Research, Jatni 752050, IndiaScientific Computing, University of Kaiserslautern-Landau (RPTU), Paul-Ehrlich-Straße, 67663 Kaiserslautern, GermanyDipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, ItalyDipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, ItalyDipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, ItalyScientific Computing, University of Kaiserslautern-Landau (RPTU), Paul-Ehrlich-Straße, 67663 Kaiserslautern, GermanyDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenScientific Computing, University of Kaiserslautern-Landau (RPTU), Paul-Ehrlich-Straße, 67663 Kaiserslautern, GermanyScientific Computing, University of Kaiserslautern-Landau (RPTU), Paul-Ehrlich-Straße, 67663 Kaiserslautern, GermanyInstitute for Experimental Particle Physics, Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanyDipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, ItalyDipartimento di Fisica e Astronomia, Università di Padova, Via F. Marzolo 8, 35131 Padova, ItalyINFN, Sezione di Padova, Via F. Marzolo 8, 35131 Padova, ItalyDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, SwedenInstitute for Experimental Particle Physics, Karlsruhe Institute of Technology, 76131 Karlsruhe, GermanyDepartment of Physics, Universidad de Oviedo and ICTEA, 33004 Oviedo, SpainINFN, Sezione di Padova, Via F. Marzolo 8, 35131 Padova, ItalyIn 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.https://www.mdpi.com/2571-712X/8/2/58particle detectorscalorimetryparticle identificationphysicsmachine learning |
| spellingShingle | Andrea De Vita Abhishek Max Aehle Muhammad Awais Alessandro Breccia Riccardo Carroccio Long Chen Tommaso Dorigo Nicolas R. Gauger Ralf Keidel Jan Kieseler Enrico Lupi Federico Nardi Xuan Tung Nguyen Fredrik Sandin Kylian Schmidt Pietro Vischia Joseph Willmore Hadron Identification Prospects with Granular Calorimeters Particles particle detectors calorimetry particle identification physics machine learning |
| title | Hadron Identification Prospects with Granular Calorimeters |
| title_full | Hadron Identification Prospects with Granular Calorimeters |
| title_fullStr | Hadron Identification Prospects with Granular Calorimeters |
| title_full_unstemmed | Hadron Identification Prospects with Granular Calorimeters |
| title_short | Hadron Identification Prospects with Granular Calorimeters |
| title_sort | hadron identification prospects with granular calorimeters |
| topic | particle detectors calorimetry particle identification physics machine learning |
| url | https://www.mdpi.com/2571-712X/8/2/58 |
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