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Boosted decision tree reweighting of simulated neutrino interactions for O (1) GeV neutrino cross-section measurements
Journal article   Open access   Peer reviewed

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

Z. Lin, S. Akhter, Z. Ahmad Dar, N. S. Alex, M. Betancourt, S. Boyd, H. Budd, G. Caceres, G. A. Díaz, J. Felix, …
Phys.Rev.D, v 114(1), 012015
01 Jul 2026
url
https://doi.org/10.1103/7zty-f2qjView
Published, Version of Record (VoR) Open

Abstract

This paper illustrates a generic method for multidimensional 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 regeneration. As an example, we test its use in a measurement of transverse kinematic imbalance of the μ- and proton in charged-current quasielastic like νμ events from the MINERvA experiment.

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