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May 8 – 10, 2023
University of Pittsburgh
US/Eastern timezone

MadNIS - Neural Multi-Channel Importance Sampling

May 8, 2023, 5:45 PM
15m
Lawrence Hall 203

Lawrence Hall 203

Speaker

Theo Heimel (Heidelberg University)

Description

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. In addition to these results from the paper “MadNIS - Neural Multi-Channel Importance Sampling”, MadNIS now interfaces to MadGraph to use its matrix elements and channel mappings. I will present preliminary results from our upcoming comparison between MadNIS and classical MadGraph for various LHC processes.

Primary authors

Theo Heimel (Heidelberg University) Ramon Winterhalder (UC Louvain) Anja Butter (Centre National de la Recherche Scientifique (FR)) Joshua Isaacson Dr Claudius Krause (Rutgers University) Prof. Fabio Maltoni (Universite Catholique de Louvain (UCL) (BE) and Università di Bologna) Olivier Mattelaer (UCLouvain) Tilman Plehn

Presentation materials