Differentiable MadNIS-Lite
Theo Heimel, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder
SciPost Phys. 18, 017 (2025) · published 15 January 2025
- doi: 10.21468/SciPostPhys.18.1.017
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Abstract
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.
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Université catholique de Louvain [UCL]
Funders for the research work leading to this publication
- Baden-Württemberg Stiftung
- Carl-Zeiss-Stiftung / Carl Zeiss Foundation
- Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
- Fonds De La Recherche Scientifique - FNRS (FNRS) (through Organization: Fonds National de la Recherche Scientifique [FNRS])