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A new way of reducing negative weights in MC@NLO
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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.
Forward citations
Cited by 4 Pith papers
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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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