Speaker
Description
Validating that a full phase-space reweighting of a Monte Carlo prediction preserves the physical fidelity of the underlying model can be challenging, and often relies on comparisons to marginalized 1D histograms of kinematic variables that can mask subtle biases of the original high-dimensional unbinned prediction. In this poster, we present a novel, unbinned approach to comparing the performance of such reweighting schemes based on the “Cross-Section-Mover’s Distance” ($\Sigma$MD), an application of Optimal Transport that quantifies the ‘work’ required to transform one theoretical prediction into another and enables an interpretation of results in terms of metric spaces. We demonstrate the utility of such a figure of merit when benchmarking reweightings performed with different metric choices (e.g. Euclidian vs. Energy-Mover’s Distance) in the context of a cell reweighting algorithm used to mitigate the effects of negative weights in a Monte Carlo simulation of Z boson production with two associated jets at next-to-leading-order (NLO) in QCD. This approach can be broadly applied in other scenarios where potential biases in full phase-space reweighting schemes should be studied in an unbinned way.