The MadNIS reloaded
Theo Heimel, Nathan Huetsch, Fabio Maltoni, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder
SciPost Phys. 17, 023 (2024) · published 29 July 2024
- doi: 10.21468/SciPostPhys.17.1.023
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Abstract
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.
Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Theo Heimel,
- 1 Nathan Huetsch,
- 2 3 Fabio Maltoni,
- 3 Olivier Mattelaer,
- 1 Tilman Plehn,
- 3 Ramon Winterhalder
- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Università di Bologna / University of Bologna [UNIBO]
- 3 Université catholique de Louvain [UCL]
Funders for the research work leading to this publication
- Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
- Fonds De La Recherche Scientifique - FNRS (FNRS) (through Organization: Fonds National de la Recherche Scientifique [FNRS])
- Fédération Wallonie-Bruxelles
- Université Catholique de Louvain