发表机构
University of York（约克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文扩展了sWeight技术，提出多维序贯加权框架，在CLAS12数据上验证了Kaon鉴别，能处理相关本底并传播统计不确定性，实现高纯度信号提取。
AI 中文摘要
在核物理和粒子物理实验中，事件选择通常是一个涉及多个相关观测量的多维分类问题。广泛使用的sWeight技术提供了一种统计上严谨的方法，利用判别变量分离信号和本底，但其传统公式通常应用于单一判别变量或同时进行多维拟合。本文提出了一种对广泛使用的sWeight技术的新颖扩展，在该扩展中，可以使用任意数量的判别变量来构建多维序贯框架。该方法允许以严谨的方式提取受控变量中的信号分布。特别关注信号和本底相关性的处理，以及统计不确定性在连续加权阶段中的传播。即使在存在大量且相关的本底的情况下，该方法也能产生高纯度的信号样本。作为基准应用，该技术的有效性在托马斯·杰斐逊国家实验室的CLAS12探测器收集的数据集中，针对级联重子搜索中的Kaon鉴别得到了验证。最终工作流程被证明在不同运行周期和束流/探测器条件下是稳健且一致的，表明与该方法相关的系统不确定性最小。统计不确定性被证明可通过基于自举的程序进行评估。该方法为多维事件加权提供了一个通用框架，旨在应用于需要在复杂相关本底存在下进行稳健信号选择的复杂分析中。
英文摘要
In nuclear and particle physics experiments, event selection is often a multidimensional classification problem which involves several correlated observables. The widely used sWeight technique provides a statistically rigorous means of separating signal and background using discriminating variables, but its conventional formulation is generally applied to a single discriminating variable or a simultaneous multidimensional fit. In this paper we present a novel extension of the widely used sWeight technique, in which an arbitrary number of discriminating variables can be used to construct a multidimensional sequential framework. This method allows the extraction of signal distributions in controlled variables in a rigorous way. Particular attention is given to the treatment of signal and background correlations, as well as the propagation of statistical uncertainties through successive weighting stages. The method yields high-purity signal samples even in the presence of large and correlated backgrounds. As a benchmark application, the validity of this technique is demonstrated on kaon identification in a dataset collected with the CLAS12 detector at the Thomas Jefferson National Laboratory for cascade baryon searches. The final workflow is shown to be robust and consistent across different run periods and beam/detector conditions, demonstrating minimal systematical uncertainties associated with the method. Statistical uncertainties are proved to be evaluable using a bootstrap-based procedure. The method provides a general framework for multidimensional event weighting and is intended for application in complex analyses requiring robust signal selection in the presence of complex correlated backgrounds.
