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Unbiased Elimination of Negative Weights in Monte Carlo Samples
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Unbiased Elimination of Negative Weights in Monte Carlo Samples
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We propose a novel method for the elimination of negative Monte Carlo event weights. The method is process-agnostic, independent of any analysis, and preserves all physical observables. We demonstrate the overall performance and systematic improvement with increasing event sample size, based on predictions for the production of a W boson with two jets calculated at next-to-leading order perturbation theory.
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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