Boosted decision tree reweighting of simulated neutrino interactions for Oð1Þ GeV neutrino cross-section measurements

Lin Z.; Akhter S.; Dar Z.A.; Alex N.S.; Betancourt M.; Boyd S.; Budd H.; Caceres G.; Díaz G.A.; Felix J.; Fields L.; Gago A.M.; Gaur P.K.; Gilligan S.M.; Gran R.; Harris D.A.; Hart A.L.; Kleykamp J.; Klustová A.; Last D.; Lozano A.; Lu X.-G.; Manly S.; Mann W.A.; McFarland K.S.; Moreno O.; Nelson J.K.; Paolone V.; Perdue G.N.; Pernas C.; Ramírez M.A.; Roy N.; Ruterbories D.; Schellman H.; Salinas C.J.S.; Correia D.S.; Sultana M.; Vaughan N.H.; Waldron A.V.; Yaeggy B.; Zazueta L. · Physical Review D (2026) · DOI: 10.1103/7zty-f2qj

Resumen

This paper illustrates a generic method for multidimensional reweighting of Oð1Þ 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. © (2026), (American Physical Society). All Rigths Reserved.

Instituciones: Universidad Nacional Mayor de San Marcos; Pontificia Universidad Católica del Perú