The MadNIS Reloaded
Fuente:
arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910546273501184 |
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| author | Heimel, Theo Huetsch, Nathan Maltoni, Fabio Mattelaer, Olivier Plehn, Tilman Winterhalder, Ramon |
| author_facet | Heimel, Theo Huetsch, Nathan Maltoni, Fabio Mattelaer, Olivier Plehn, Tilman Winterhalder, Ramon |
| contents | In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_01548 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The MadNIS Reloaded Heimel, Theo Huetsch, Nathan Maltoni, Fabio Mattelaer, Olivier Plehn, Tilman Winterhalder, Ramon High Energy Physics - Phenomenology High Energy Physics - Experiment Computational Physics In pursuit of precise and fast theory predictions for the LHC, we present an implementation of the MadNIS method in the MadGraph event generator. A series of improvements in MadNIS further enhance its efficiency and speed. We validate this implementation for realistic partonic processes and find significant gains from using modern machine learning in event generators. |
| title | The MadNIS Reloaded |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2311.01548 |