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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2311.17175 |
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| _version_ | 1866912001396047872 |
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| author | Butter, Anja Jezo, Tomas Klasen, Michael Kuschick, Mathias Schweitzer, Sofia Palacios Plehn, Tilman |
| author_facet | Butter, Anja Jezo, Tomas Klasen, Michael Kuschick, Mathias Schweitzer, Sofia Palacios Plehn, Tilman |
| contents | Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_17175 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Kicking it Off(-shell) with Direct Diffusion Butter, Anja Jezo, Tomas Klasen, Michael Kuschick, Mathias Schweitzer, Sofia Palacios Plehn, Tilman High Energy Physics - Phenomenology Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We present a novel method to transform high-dimensional distributions based on a diffusion neural network and use it to generate a process with off-shell kinematics from the much simpler on-shell one. Applied to a toy example of top pair production at LO we show how our method generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features. |
| title | Kicking it Off(-shell) with Direct Diffusion |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2311.17175 |