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DOE OSTI · 2007976

MadNIS - Neural multi-channel importance sampling

Heimel, Theo·Winterhalder, Ramon·Butter, Anja·Isaacson, Joshua·Krause, Claudius·Maltoni, Fabio·Mattelaer, Olivier·Plehn, Tilman
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Abstract

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.

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Heimel, Theo, Winterhalder, Ramon, Butter, Anja, Isaacson, Joshua, Krause, Claudius, Maltoni, Fabio, Mattelaer, Olivier, Plehn, Tilman. 2023-10-06. MadNIS - Neural multi-channel importance sampling. https://doi.org/10.21468/scipostphys.15.4.141

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