MadNIS -- Neural Multi-Channel Importance Sampling
arXiv:2212.06172 · doi:10.21468/SciPostPhys.15.4.141
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.
33 pages, 15 figures, minor fixes to v1