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

show 33 more authors

G.A. D\'iaz J. Felix L. Fields A.M. Gago P.K.Gaur S.M. Gilligan R. Gran D.A. Harris A.L. Hart J. Kleykamp A. Klustov\'a D. Last A. Lozano X.-G. Lu S. Manly W.A. Mann K.S. McFarland O. Moreno J.K. Nelson V. Paolone G.N. Perdue C. Pernas M.A. Ram\'irez N. Roy D. Ruterbories H. Schellman C. J. Solano Salinas D. S. Correia M. Sultana N.H. Vaughan A.V. Waldron B. Yaeggy L. Zazueta (The MINERvA Collaboration)

Authors on Pith no claims yet

classification ✦ hep-ex

keywords neutrinoreweightingboostedcarlodecisiondetectoreventsmonte

0 comments

read the original 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.

This paper has not been read by Pith yet.

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

discussion (0)