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Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples
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Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples
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We demonstrate that cell resampling can eliminate the bulk of negative event weights in large event samples of high multiplicity processes without discernible loss of accuracy in the predicted observables. The application of cell resampling to much larger data sets and higher multiplicity processes such as vector boson production with up to five jets has been made possible by improvements in the method paired with drastic enhancement of the computational efficiency of the implementation.
Forward citations
Cited by 4 Pith papers
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Precision Cell Resampling with a Relative and Resonant Aware Metric
A resonance-sensitive metric using relative transverse momenta allows cell resampling to reduce negative weights in NLO W+2jets samples while preserving resonance predictions with high accuracy.
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Stay Positive: Neural Refinement of Sample Weights
Neural refinement of Monte Carlo sample weights via phase-space scaling and a new resampling protocol that maintains averages and uncertainties.
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Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights
IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
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Matrix element method at NLO: A fine proof of concept in POWHEG
Proof-of-concept for NLO matrix element method via POWHEG projections applied to fully leptonic WW production in SMEFT, demonstrating near-optimal classification of BSM versus SM events using lepton correlations.
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