Differentiable MadNIS-Lite
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866917892400873472 |
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| author | Heimel, Theo Mattelaer, Olivier Plehn, Tilman Winterhalder, Ramon |
| author_facet | Heimel, Theo Mattelaer, Olivier Plehn, Tilman Winterhalder, Ramon |
| contents | Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MadNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MadNIS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01486 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentiable MadNIS-Lite Heimel, Theo Mattelaer, Olivier Plehn, Tilman Winterhalder, Ramon High Energy Physics - Phenomenology High Energy Physics - Experiment Computational Physics Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MadNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MadNIS. |
| title | Differentiable MadNIS-Lite |
| topic | High Energy Physics - Phenomenology High Energy Physics - Experiment Computational Physics |
| url | https://arxiv.org/abs/2408.01486 |