The MadNIS Reloaded

by Ramon Winterhalder (UC Louvain)

Monday 4 Dec 2023, 11:00 → 13:00 Europe/Zurich

Aula Caldirola (University of Milan-Statale)

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. 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.