Speaker
Ramon Winterhalder
(UCLouvain)
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. By integrating buffered training for potentially expensive integrands, VEGAS initialization, symmetry-aware channels, and stratified training, we elevate the performance in both efficiency and accuracy. We empirically validate these enhancements through rigorous tests on diverse LHC processes, including VBS and W+jets
Primary authors
Nathan Huetsch
(Heidelberg University)
Ramon Winterhalder
(UCLouvain)
Theo Heimel
(Heidelberg University)
Tilman Plehn
(Universität Heidelberg, ITP)
