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On the reduction of negative weights in MC@NLO-type matching procedures
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We show how a careful analysis of the behaviour of a parton shower Monte Carlo in the vicinity of the soft and collinear regions allows one to formulate a modified MC@NLO-matching prescription that reduces the number of negative-weight events with respect to that stemming from the standard MC@NLO procedure. As a first practical application of such a prescription, that we dub MC@NLO-$\Delta$, we have implemented it in the MadGraph5_aMC@NLO framework, by employing the Pythia8 Monte Carlo. We present selected MC@NLO-$\Delta$ results at the 13 TeV LHC, and compare them with MC@NLO ones. We find that the former predictions are consistent with the latter ones within the typical matching systematics, and that the reduction of negative-weight events is significant.
Forward citations
Cited by 6 Pith papers
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Stay Positive: Neural Refinement of Sample Weights
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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
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Matrix element method at NLO: A fine proof of concept in POWHEG
Proof-of-concept for NLO matrix element method via POWHEG projections applied to fully leptonic WW production in SMEFT, demonstrating near-optimal classification of BSM versus SM events using lepton correlations.
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MAcNLOPS for ZZ Pair Production at the LHC
MAcNLOPS is implemented for ZZ production at the LHC, eliminating negative H weights via a shower veto on S events while agreeing with MC@NLO except for small low-pT effects.
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Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
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