# Unbiased Elimination of Negative Weights in Monte Carlo Samples

@inproceedings{Andersen2021UnbiasedEO, title={Unbiased Elimination of Negative Weights in Monte Carlo Samples}, author={Jeppe R. Andersen and Andreas Maier}, year={2021} }

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. ar X iv :2 10 9. 07 85 1v 1 [ he pph ] 1 6 Se p 20 21

#### 3 Citations

Reducing negative weights in Monte Carlo event generation with Sherpa

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- 2021

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… Expand

Multiplicative-Accumulative matching of NLO calculations with parton showers

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We propose a new approach for combining next-to-leading order (NLO) and parton shower (PS) calculations so as to obtain three core features: (a) applicability to general showers, as with the MC@NLO… Expand

HL-LHC Computing Review Stage-2, Common Software Projects: Event Generators

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- 2021

This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group (WG), as an input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC)… Expand

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