TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.15.4.141
TI  - MadNIS - Neural multi-channel importance sampling
PY  - 2023/10/06
UR  - https://scipost.org/SciPostPhys.15.4.141
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 15
IS  - 4
SP  - 141
A1  - Heimel, Theo
AU  - Winterhalder, Ramon
AU  - Butter, Anja
AU  - Isaacson, Joshua
AU  - Krause, Claudius
AU  - Maltoni, Fabio
AU  - Mattelaer, Olivier
AU  - Plehn, Tilman
AB  - Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling, to improve classical methods for numerical integration. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance.
ER  -

@Article{10.21468/SciPostPhys.15.4.141,
	title={{MadNIS - Neural multi-channel importance sampling}},
	author={Theo Heimel and Ramon Winterhalder and Anja Butter and Joshua Isaacson and Claudius Krause and Fabio Maltoni and Olivier Mattelaer and Tilman Plehn},
	journal={SciPost Phys.},
	volume={15},
	pages={141},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.15.4.141},
	url={https://scipost.org/10.21468/SciPostPhys.15.4.141},
}