Work

MadNIS at NLO

Year: 2026

Type: preprint

Abstract: 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 calibrated uncertainties guarantees reliable precision across phase space. For real emission, we stick to the standard FKS subtraction and train sector-conditioned surrogates of the regularized integrands away from divergences. MadNIS then uses multi-channel mappings and FKS sectors as conditions. We validate our approach for electron-positron scattering to three and four jets and find significant speed-ups and variance reduction in the integration.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2603.22407

Authors: De Crescenzo, Giovanni, Villadamigo, Javier Mariño, Elmer, Nina, Heimel, Theo, Plehn, Tilman, Winterhalder, Ramon, Zaro, Marco

Institutions: Heidelberg University, University of Cambridge, UCLouvain, University of Milan, Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Istituto Nazionale di Fisica Nucleare, Sezione di Milano Bicocca

Topic: Particle physics theoretical and experimental studies

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Deutsche Forschungsgemeinschaft, Fonds De La Recherche Scientifique - FNRS

Awards: G1741598991, TRR 257: Phänomenologische Elementarteilchenphysik nach der HiggsEntdeckung, G2645866196, G2805343336, G468049937, G7291426837, G7316740004, G972786147

OpenAlex ID: W7140315776

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