Unbiased Elimination of Negative Weights in Monte Carlo Samples

Abstract

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.

arXiv comment22 pages, 7 figures

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Cited by in corpus (9)

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• A CT18 global PDF fit at the leading order in QCD
• Multiplicative-Accumulative matching of NLO calculations with parton showers
• Logarithmically-accurate and positive-definite NLO shower matching
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• Reducing negative weights in Monte Carlo event generation with Sherpa
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• Monte Carlo Event Generators
• Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification