Year: 2026
Type: preprint
Abstract: We extend neural multi-channel importance sampling in MadNIS to full proton-proton processes in a production-ready event-generation setup. We benchmark a wide range of LHC processes, including multi-jet, vector-boson, top-quark, and Higgs production, and obtain event-generation speed-ups of up to a factor of 250 without process-specific hyperparameter tuning. We quantify how the speed-up scales with training time and final-state multiplicity, and when the training cost amortizes. MadNIS will become the default for phase-space integration and event generation at leading order in MadGraph.
Source: arXiv (Cornell University)
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7221750368