2 papers
hep-ph2026
MadNIS at NLO
Giovanni De Crescenzo, Javier Mariño Villadamigo, Nina Elmer +4
We combine fast amplitude surrogates with neural importance sampling to accelerate NLO calculations. For virtual corrections, a learned ratio to the Born matrix element with calibr…
hep-ph2025
Evaluating the faithfulness of PDF uncertainties in the presence of inconsistent data
Andrea Barontini, Mark N. Costantini, Giovanni De Crescenzo +2
We critically assess the robustness of uncertainties on parton distribution functions (PDFs) determined using neural networks from global sets of experimental data collected from m…