Unbiased Elimination of Negative Weights in Monte Carlo Samples

TL;DR

This paper introduces a process-agnostic method to eliminate negative weights in Monte Carlo samples, ensuring physical observables are preserved and improving with larger sample sizes, demonstrated on W boson production at NLO.

Contribution

A novel, process-independent technique for removing negative weights in Monte Carlo simulations that maintains all physical observables.

Findings

Method effectively removes negative weights.

Performance improves with larger event samples.

Validated on W boson production at NLO.

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.