Unbiased elimination of negative weights in Monte Carlo samples
Jeppe R. Andersen, Andreas Maier
Abstract
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.
Topics & Concepts
Monte Carlo methodObservableStatistical physicsEvent (particle physics)Dynamic Monte Carlo methodMonte Carlo method in statistical physicsMonte Carlo molecular modelingPerturbation theory (quantum mechanics)Sample size determinationHybrid Monte CarloPhysicsMathematicsStatisticsParticle physicsMarkov chain Monte CarloQuantum mechanicsParticle physics theoretical and experimental studiesQuantum Chromodynamics and Particle InteractionsHigh-Energy Particle Collisions Research