Extrapolating Jet Radiation with Autoregressive Transformers
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909986957819904 |
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| author | Butter, Anja Charton, François Villadamigo, Javier Mariño Ore, Ayodele Plehn, Tilman Spinner, Jonas |
| author_facet | Butter, Anja Charton, François Villadamigo, Javier Mariño Ore, Ayodele Plehn, Tilman Spinner, Jonas |
| contents | Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet radiation, and offer the possibility to generalize to higher multiplicities. We show how transformers can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets. For this extrapolation, bootstrapping training data and training with modifications of the likelihood loss can be used. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extrapolating Jet Radiation with Autoregressive Transformers Butter, Anja Charton, François Villadamigo, Javier Mariño Ore, Ayodele Plehn, Tilman Spinner, Jonas High Energy Physics - Phenomenology Machine Learning Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet radiation, and offer the possibility to generalize to higher multiplicities. We show how transformers can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets. For this extrapolation, bootstrapping training data and training with modifications of the likelihood loss can be used. |
| title | Extrapolating Jet Radiation with Autoregressive Transformers |
| topic | High Energy Physics - Phenomenology Machine Learning |
| url | https://arxiv.org/abs/2412.12074 |