Cell Reweighting Algorithms for Pathological Weight Mitigation in LHC Simulations using Optimal Transport

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 and beyond results in a number of negative weighted events. These weights increase strain on computational resources and can be pathological when used in machine learning analyses. We have developped a post hoc ‘cell reweighting’ scheme by imposing a metric in the multidimensional space of events so that nearby events on this manifold are reweighted together. We compare the performance of the algorithm with different choices of metric. We explicitly demonstrate the performance of the algorithm by implementing the reweighting scheme on simulated data of a Z boson and two jets produced at NLO accuracy.