The MadNIS Reloaded

Maltoni, Fabio; Mattelaer, Olivier; Winterhalder, Ramon

doi:10.21468/SciPostPhys.17.1.023

The MadNIS Reloaded

Maltoni, Fabio

;

Mattelaer, Olivier

;

Winterhalder, Ramon

;et.al.

(2024) SciPost Physics — Vol. 17 (2024)

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Authors

Maltoni, FabioUCLouvain
Author
Mattelaer, OlivierUCLouvain
Author
Winterhalder, RamonUCLouvain
Author
et. al.

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.

Affiliations

UCLouvainSST/IRMP - Institut de recherche en mathématique et physique

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Maltoni, F., Mattelaer, O., Winterhalder, R., & et al. (2024). The MadNIS Reloaded. SciPost Physics, 17. https://doi.org/10.21468/SciPostPhys.17.1.023 (Original work published 2024)