Boosted decision tree reweighting of simulated neutrino interactions for $O(1)$ GeV neutrino cross section measurements

Abstract

This paper illustrates a generic method for multi-dimensional reweighting of $O(1)$ GeV neutrino interaction Monte Carlo samples. The reweighting is based on a Boosted Decision Tree algorithm trained on high-dimensional space in detector final-state observables. This enables one generator's events to be reweighted so that its reconstructed particle content and kinematics distributions, as well as detector efficiency, match those of a target model. The approach establishes an efficient way to reuse legacy Monte Carlo data, avoiding re-generation. As an example, we test its use in a measurement of transverse kinematic imbalance of the $\mu^-$ and proton in charged-current quasielastic like $\nu_\mu$ events from the MINERvA experiment.