标题： 针对$O(1)$ GeV 中微子截面测量的模拟中微子相互作用的提升决策树重新加权 显示英文标题

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

摘要： 本文展示了一种多维重加权的通用方法，用于$O(1)$ GeV 中微子相互作用蒙特卡罗样本的重加权。 重加权基于在探测器最终状态可观测量的高维空间上训练的提升决策树算法。 这使得一个生成器的事件可以被重加权，使其重建的粒子内容和运动学分布以及探测器效率与目标模型相匹配。 该方法建立了一种高效的方法来重用遗留蒙特卡罗数据，避免了重新生成。 作为示例，我们测试了其在 MINERvA 实验中对带电电流准弹性类似$\nu_\mu$事例中$\mu^-$和质子的横向运动学不平衡测量中的应用。 显示英文摘要

摘要： 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.