A Positive Resampler for Monte Carlo Events with Negative Weights

TL;DR

The paper introduces the Positive Resampler, a method that converts negative-weighted Monte Carlo events into positive weights, ensuring accurate physical distributions and reducing computational resource needs.

Contribution

It presents a novel resampling technique that guarantees positive event weights while maintaining correct observable distributions in collider simulations.

Findings

Successfully applied to W boson production with multiple jets

Ensures positive weights for all physical distributions

Reduces size and resource demands of event samples

Abstract

We propose the Positive Resampler to solve the problem associated with event samples from state-of-the-art predictions for scattering processes at hadron colliders typically involving a sizeable number of events contributing with negative weight. The proposed method guarantees positive weights for all physical distributions, and a correct description of all observables. A desirable side product of the method is the possibility to reduce the size of event samples produced by General Purpose Event Generators, thus lowering the resource demands for subsequent computing-intensive event processing steps. We demonstrate the viability and efficiency of our approach by considering its application to a next-to-leading order + parton shower merged prediction for the production of a $W$ boson in association with multiple jets.