تقرير
MadNIS -- Neural Multi-Channel Importance Sampling
| العنوان: | MadNIS -- Neural Multi-Channel Importance Sampling |
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| المؤلفون: | Heimel, Theo, Winterhalder, Ramon, Butter, Anja, Isaacson, Joshua, Krause, Claudius, Maltoni, Fabio, Mattelaer, Olivier, Plehn, Tilman |
| المصدر: | SciPost Phys. 15, 141 (2023) |
| سنة النشر: | 2022 |
| المجموعة: | High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | High Energy Physics - Phenomenology, High Energy Physics - Experiment, Physics - Computational Physics |
| الوصف: | Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance. Comment: 33 pages, 15 figures, minor fixes to v1 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.21468/SciPostPhys.15.4.141 |
| URL الوصول: | http://arxiv.org/abs/2212.06172 |
| رقم الأكسشن: | edsarx.2212.06172 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.21468/SciPostPhys.15.4.141 |
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