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SUMMARY:Positive Smeared Matrix Elements at NLO
DTSTART:20260410T193000Z
DTEND:20260410T213000Z
DTSTAMP:20260414T232200Z
UID:indico-event-10297@indico.slac.stanford.edu
DESCRIPTION:Speakers: Andrew Larkoski\n\nThe issue of negative weights in 
 the simulation of particle collider events at higher orders in perturbatio
 n theory can significantly reduce numerical precision\, for a given statis
 tical sample size.  Several methods for reducing negative event weights h
 ave been proposed\, including resampling techniques that involve summing o
 r ``smearing over'' nearby events on phase space to ensure positivity.  S
 uch methods have typically used machine learning algorithms to perform the
  resampling of the data ensemble\, but effectively use no physics to infor
 m it.  In this talk\, I will introduce an event smearing algorithm that e
 xploits the universality of soft and collinear divergences in quantum chro
 modynamics\, explicitly calculating all necessary components at next-to-le
 ading order.\n\nhttps://indico.slac.stanford.edu/event/10297/
LOCATION:48/2-224 - Madrone (SLAC)
URL:https://indico.slac.stanford.edu/event/10297/
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