Speakers
Description
As the accuracy of experimental results increase 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, and can be pathological in the context of machine learning. We have developed a post hoc ‘cell reweighting’ scheme by applying an IRC-safe metric in the multidimensional metric space of events so that nearby events in this space are reweighted together. This metric is implemented using Optimal Transport techniques, borrowing from the field of computer vision to solve a longstanding problem in computational particle physics. We compare the performance of the algorithm with different choices of metric, and explicitly demonstrate the performance of the algorithm by implementing the reweighting scheme on simulated events with a Z boson and two jets produced at NLO accuracy.