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Reducing negative weights in Monte Carlo event generation with Sherpa
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An increase in theoretical precision of Monte Carlo event generators is typically accompanied by an increased need for computational resources. One major obstacle are negative weighted events, which appear in Monte Carlo simulations with higher perturbative accuracy. While they can be handled somewhat easily in fixed-order calculations, they are a major concern for particle level event simulations. In this article, the origin of negative weights in the S-MC@NLO method is reviewed and mechanisms to reduce the negative weight fraction in simulations with the Sherpa event generator are presented, with a focus on V+jets and tt+jets simulations.
Forward citations
Cited by 4 Pith papers
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Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights
IRC-safe optimal-transport metrics (EMD, sEMD) enable lower-bias cell resampling of negative-weight NLO Monte Carlo events without intermediate jet clustering.
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A resonance-aware MC@NLO QCD+EW-matched calculation of lepton-pair production
First automated MC@NLO matching of NLO QCD+EW to an interleaved QCD+QED parton shower with resonance-aware dipole subtraction, validated for Drell-Yan lepton-pair production.
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A Demonstration of ARCANE Reweighting: Reducing the Sign Problem in the MC@NLO Generation of $e^+ e^- \rightarrow q \bar{q} + 1\, jet$ Events
ARCANE reweighting cuts the post-unweighting negative-event fraction in e+e- -> q qbar + 1 jet MC@NLO generation from about 2.25% to below 10^-5 while preserving the visible event distributions.
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ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
ARCANE reweighting adds a carefully designed, zero-average correction to event weights so that positive and negative pathways to the same event cancel, preserving all physical distributions.
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