A new way of reducing negative weights in MC@NLO

TL;DR

This paper presents Born spreading, a novel technique to significantly reduce negative-weight events in MC@NLO simulations by redistributing Born matrix elements, without biasing physical results.

Contribution

The paper introduces Born spreading, a new method that decreases negative-weight events in MC@NLO by redistributing Born matrix elements, with minimal computational overhead.

Findings

Significant reduction in negative-weight events achieved.

No bias introduced in physical distributions.

Method is computationally efficient.

Abstract

We introduce a new technique, that we dub Born spreading, aimed at reducing the number of negative-weight $\mathbb S$ events in the MC@NLO matching of NLO calculations with parton-shower simulations. We show that such a technique, based on a re-distribution of Born matrix elements in the radiative phase space, achieves a sizeable reduction of negative-weight events at little computational cost. The method does not induce any biases in physical distributions.