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Unbiased Elimination of Negative Weights in Monte Carlo Samples

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If you have a question about this talk, please contact Heribertus Bayu Hartanto .

State-of-the-art Monte Carlo event simulations typically involve a sizeable fraction of events with negative weights. This means that often at least an order of magnitude more events have to be generated to reach the same statistical significance as in the absence of negative weights. We propose a method to eliminate negative weights in arbitrary event samples. The method is based on redistributing weights between practically indistinguishable events and preserves all physical predictions for observables. We demonstrate its performance for the production of a W boson with two jets at next-to-leading order in perturbation theory.

This talk is part of the Cavendish HEP Seminars series.

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