18th International Workshop on Boosted Object Phenomenology, Reconstruction, Measurements, and Searches at Colliders

Session

Negative-weight mitigation for MC simulation

Jul 16, 2026, 2:00 PM

1. Resummed Distribution Functions: Making Perturbation Theory Positive and Normalized

Radha Mastandrea

7/16/26, 2:00 PM

1

Talk

Fixed-order perturbative calculations for differential cross sections can suffer from non-physical artifacts: they can be non-positive, non-normalizable, and non-finite, none of which occur in experimental measurements. We propose a framework, the Resummed Distribution Function (RDF), that, given a perturbative calculation for an observable to some finite order in $\alpha_s$, will ``resum''...

18. Cell Reweighting and Unbinned Validation for Negative-Weight Mitigation using Optimal Transport

Jennifer Roloff (Brown University (US))

7/16/26, 2:20 PM

Talk

As the accuracy of experimental results increases in high energy physics, so too must the precision of Monte Carlo simulations. Currently, event generation at next-to-leading order (NLO) accuracy in QCD and beyond results in the production of negatively-weighted events. The presence of these weights increases strain on computational resources by degrading the statistical power of MC samples,...

24. Positive Smeared Matrix Elements at Next-to-Leading Order

Rikab Gambhir (University of Cincinnati)

7/16/26, 2:40 PM

Talk

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...

52. The Latent Information Geometry of Jet Classification

Sophia Vent

7/16/26, 3:00 PM

Talk

Latent representations are an important theme in modern machine learning. Any network training with the notion of locality introduces a latent geometry which we can analyze with the help of differential geometry, specifically information geometry. We introduce the main concepts needed to analyze learned latent geometries, specifically curvature and nonmetricities, and show how they can be used...