Title : MadNIS - Neural networks for multi-channel integration
Speaker : Dr. R. Winterhalder (CP3, UCLouvain, Belgium)
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. In addition to these results from our paper (https://arxiv.org/abs/2212.06172), MadNIS now interfaces with MadGraph, and I will present preliminary results from our upcoming comparison between MadNIS and classical MadGraph for various LHC processes.