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SUMMARY:Unbiased Elimination of Negative Weights in Monte Carlo Samples  -
  Andreas Maier (DESY Zeuthen)
DTSTART:20220315T160000Z
DTEND:20220315T170000Z
UID:TALK167525@talks.cam.ac.uk
CONTACT:Heribertus Bayu Hartanto
DESCRIPTION:State-of-the-art Monte Carlo event simulations typically invol
 ve a\nsizeable fraction of events with negative weights. This means that\n
 often at least an order of magnitude more events have to be generated\nto 
 reach the same statistical significance as in the absence of\nnegative wei
 ghts. We propose a method to eliminate negative weights in\narbitrary even
 t samples. The method is based on redistributing weights\nbetween practica
 lly indistinguishable events and preserves all\nphysical predictions for o
 bservables. We demonstrate its performance\nfor the production of a W boso
 n with two jets at next-to-leading order\nin perturbation theory.
LOCATION:Ryle Seminar Room
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