CaloHadronic: a diffusion model for the generation of hadronic showers
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915762960072704 |
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| author | Buss, Thorsten Gaede, Frank Kasieczka, Gregor Korol, Anatolii Krüger, Katja McKeown, Peter Mozzanica, Martina |
| author_facet | Buss, Thorsten Gaede, Frank Kasieczka, Gregor Korol, Anatolii Krüger, Katja McKeown, Peter Mozzanica, Martina |
| contents | Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, are very efficient. We present a transformer-based extension to previous architectures which were developed for simulating electromagnetic showers in the highly granular electromagnetic calorimeter of the International Large Detector, ILD. The attention mechanism now allows us to generate complex hadronic showers with more pronounced substructure across both the electromagnetic and hadronic calorimeters. This is the first time that machine learning methods are used to holistically generate showers across the electromagnetic and hadronic calorimeter in highly granular imaging calorimeter systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21720 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CaloHadronic: a diffusion model for the generation of hadronic showers Buss, Thorsten Gaede, Frank Kasieczka, Gregor Korol, Anatolii Krüger, Katja McKeown, Peter Mozzanica, Martina Instrumentation and Detectors Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, are very efficient. We present a transformer-based extension to previous architectures which were developed for simulating electromagnetic showers in the highly granular electromagnetic calorimeter of the International Large Detector, ILD. The attention mechanism now allows us to generate complex hadronic showers with more pronounced substructure across both the electromagnetic and hadronic calorimeters. This is the first time that machine learning methods are used to holistically generate showers across the electromagnetic and hadronic calorimeter in highly granular imaging calorimeter systems. |
| title | CaloHadronic: a diffusion model for the generation of hadronic showers |
| topic | Instrumentation and Detectors Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.21720 |