Matches in SemOpenAlex for { <https://semopenalex.org/work/W4311552529> ?p ?o ?g. }
- W4311552529 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. We develop an efficient bi-directional setup based on an invertible network, combining online and buffered training for potentially expensive integrands. We illustrate our method for the Drell-Yan process with an additional narrow resonance." @default.
- W4311552529 created "2022-12-27" @default.
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- W4311552529 date "2023-10-06" @default.
- W4311552529 modified "2023-10-16" @default.
- W4311552529 title "MadNIS - Neural multi-channel importance sampling" @default.
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- W4311552529 doi "https://doi.org/10.21468/scipostphys.15.4.141" @default.
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