Affiliation:
1. Heidelberg University
2. Université catholique de Louvain
3. University of Bologna
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.
Funder
Deutsche Forschungsgemeinschaft
Fonds De La Recherche Scientifique - FNRS
Fédération Wallonie-Bruxelles
Université Catholique de Louvain
Cited by
1 articles.
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