The MadNIS Reloaded - Boosting MG5aMC with Neural Networks

Octubre 19, 2023
De 3:00pm hasta 4:00pm

IFT Seminar Room/Red Room

Specialist level
Ramon Winterhalder
Université Catholique de Louvain

IFT Seminar Room/Red Room


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 of the MadNIS framework. We empirically validate these enhancements through rigorous tests on diverse LHC processes, including VBS and W+jets.