TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.17.1.023
TI  - The MadNIS reloaded
PY  - 2024/07/29
UR  - https://www.scipost.org/SciPostPhys.17.1.023
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 17
IS  - 1
SP  - 023
A1  - Heimel, Theo
AU  - Huetsch, Nathan
AU  - Maltoni, Fabio
AU  - Mattelaer, Olivier
AU  - Plehn, Tilman
AU  - Winterhalder, Ramon
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.17.1.023,
	title={{The MadNIS reloaded}},
	author={Theo Heimel and Nathan Huetsch and Fabio Maltoni and Olivier Mattelaer and Tilman Plehn and Ramon Winterhalder},
	journal={SciPost Phys.},
	volume={17},
	pages={023},
	year={2024},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.17.1.023},
	url={https://scipost.org/10.21468/SciPostPhys.17.1.023},
}