Positive Smeared Matrix Elements at NLO

by Andrew Larkoski

Friday 10 Apr 2026, 12:30 → 14:30 America/Los_Angeles

48/2-224 - Madrone (SLAC)

The issue of negative weights in the simulation of particle collider events at higher orders in perturbation theory can significantly reduce numerical precision, for a given statistical sample size.  Several methods for reducing negative event weights have been proposed, including resampling techniques that involve summing or ``smearing over'' nearby events on phase space to ensure positivity.  Such methods have typically used machine learning algorithms to perform the resampling of the data ensemble, but effectively use no physics to inform it.  In this talk, I will introduce an event smearing algorithm that exploits the universality of soft and collinear divergences in quantum chromodynamics, explicitly calculating all necessary components at next-to-leading order.