- Open Access
Boosted decision tree reweighting of simulated neutrino interactions for neutrino cross-section measurements
Phys. Rev. D 114, 012015 – Published 20 July, 2026
DOI: https://doi.org/10.1103/7zty-f2qj
Abstract
This paper illustrates a generic method for multidimensional reweighting of 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 regeneration. As an example, we test its use in a measurement of transverse kinematic imbalance of the and proton in charged-current quasielastic like events from the MINERvA experiment.
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