Fast, accurate, and precise detector simulation with vision transformers
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915754425712640 |
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| author | Favaro, Luigi Giammanco, Andrea Krause, Claudius |
| author_facet | Favaro, Luigi Giammanco, Andrea Krause, Claudius |
| contents | The speed and fidelity of detector simulations in particle physics pose compelling questions about LHC analysis and future colliders. The sparse high-dimensional data, combined with the required precision, provide a challenging task for modern generative networks. We present a comparison between solutions with different trade-offs, including accurate Conditional Flow Matching and faster coupling-based Normalising Flows. Vision Transformers allows us to emulate the energy deposition from detailed Geant4 simulations. We evaluate the networks using high-level observables, neural network classifiers, and sampling timings, showing minimum deviations from Geant4 while achieving faster generation. We use the CaloChallenge benchmark datasets for reproducibility and further development. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_25169 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast, accurate, and precise detector simulation with vision transformers Favaro, Luigi Giammanco, Andrea Krause, Claudius High Energy Physics - Phenomenology High Energy Physics - Experiment The speed and fidelity of detector simulations in particle physics pose compelling questions about LHC analysis and future colliders. The sparse high-dimensional data, combined with the required precision, provide a challenging task for modern generative networks. We present a comparison between solutions with different trade-offs, including accurate Conditional Flow Matching and faster coupling-based Normalising Flows. Vision Transformers allows us to emulate the energy deposition from detailed Geant4 simulations. We evaluate the networks using high-level observables, neural network classifiers, and sampling timings, showing minimum deviations from Geant4 while achieving faster generation. We use the CaloChallenge benchmark datasets for reproducibility and further development. |
| title | Fast, accurate, and precise detector simulation with vision transformers |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.25169 |