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.

22 pages, 7 figures

References in corpus (7)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Event generation with SHERPA 1.1
• LHAPDF6: parton density access in the LHC precision era
• Robust Independent Validation of Experiment and Theory: Rivet version 3
• Measurements of the W production cross sections in association with jets with the ATLAS detector
• A Positive-Weight Next-to-Leading-Order Monte Carlo for Z Pair Hadroproduction

Cited by in corpus (9)

• Event Generators for High-Energy Physics Experiments
• 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
• Efficient negative-weight elimination in large high-multiplicity Monte Carlo event samples
• Reducing negative weights in Monte Carlo event generation with Sherpa
• Stay Positive: Neural Refinement of Sample Weights
• Monte Carlo Event Generators
• Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification