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Monte Carlo event generators are an essential tool for data analysis incollider physics. To include subleading quantum corrections, these generatorsoften need to produce negative weight events, which leads to statisticaldilution of the datasets and downstream computational costs for detectorsimulation. Building on the recent proposal of a positive resampler method torebalance weights within histogram bins, we introduce neural resampling: anunbinned approach to Monte Carlo reweighting based on neural networks thatscales well to high-dimensional and variable-dimensional phase space. We payparticular attention to preserving the statistical properties of the eventsample, such that neural resampling not only maintains the mean value of anyobservable but also its Monte Carlo uncertainty. This uncertainty preservationscheme is general and can also be applied to binned (non-neural network)resampling. To illustrate our neural resampling approach, we present a casestudy from the Large Hadron Collider of top quark pair production atnext-to-leading order matched to a parton shower.

A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties