Speaker
Description
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 or even eliminating 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. We 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. We are able to show that the effect of this smearing does introduce numerical errors in the now-positive event weights, but the size of these effects are suppressed by powers of the smearing radius and are typically smaller than unknown next-to-next-to-leading order contributions. We demonstrate this procedure in two- and three-jet events in $e^+e^-$ collisions, and provide first results for its extension to the smearing of events at next-to-next-to-leading order.