Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915028400078848 |
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| author | Kobylianskii, Dmitrii Soybelman, Nathalie Kakati, Nilotpal Dreyer, Etienne Nachman, Benjamin Gross, Eilam |
| author_facet | Kobylianskii, Dmitrii Soybelman, Nathalie Kakati, Nilotpal Dreyer, Etienne Nachman, Benjamin Gross, Eilam |
| contents | The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles Kobylianskii, Dmitrii Soybelman, Nathalie Kakati, Nilotpal Dreyer, Etienne Nachman, Benjamin Gross, Eilam High Energy Physics - Experiment High Energy Physics - Phenomenology The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets. |
| title | Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles |
| topic | High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2405.10106 |