CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
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arXiv
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| Format: | Preprint |
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2025
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| author | Buss, Thorsten Day-Hall, Henry Gaede, Frank Kasieczka, Gregor Krüger, Katja Korol, Anatolii Madlener, Thomas McKeown, Peter Mozzanica, Martina Valente, Lorenzo |
| author_facet | Buss, Thorsten Day-Hall, Henry Gaede, Frank Kasieczka, Gregor Krüger, Katja Korol, Anatolii Madlener, Thomas McKeown, Peter Mozzanica, Martina Valente, Lorenzo |
| contents | We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcloud model can employ angular conditioning to replicate photons at all incident angles. Showers produced by this model can be used across the whole detector barrel, due to specially produced position agnostic training data. With this flexibility, the model is usable in a full simulation and reconstruction chain, which offers a further handle for evaluating physics performance of the model. As inference time is a crucial consideration for a generative model, the pre-processing and hyperparameters are aggressively optimised, achieving a speed up factor of two orders of magnitude over Geant4 at inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation Buss, Thorsten Day-Hall, Henry Gaede, Frank Kasieczka, Gregor Krüger, Katja Korol, Anatolii Madlener, Thomas McKeown, Peter Mozzanica, Martina Valente, Lorenzo Instrumentation and Detectors High Energy Physics - Experiment Computational Physics We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcloud model can employ angular conditioning to replicate photons at all incident angles. Showers produced by this model can be used across the whole detector barrel, due to specially produced position agnostic training data. With this flexibility, the model is usable in a full simulation and reconstruction chain, which offers a further handle for evaluating physics performance of the model. As inference time is a crucial consideration for a generative model, the pre-processing and hyperparameters are aggressively optimised, achieving a speed up factor of two orders of magnitude over Geant4 at inference. |
| title | CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation |
| topic | Instrumentation and Detectors High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2511.01460 |