Machine learning (ML) has been widely applied in high energy physics to help the physical community in particle classification and data analysis. Here we describe the application of machine learning to solve the problem of classifying background and signal events for the DEAP-3600 dark matter search experiment (SNOLAB, Canada). We apply Boosted Decision Trees (BDT) algorithm of ML with improvements from Extra Trees and eXtra Gradient Boosting (XGBoost) methods.

中文翻译：

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DEAP-3600 实验中对颈部 alpha 事件区分的增强决策树方法

机器学习 (ML) 已广泛应用于高能物理，以帮助物理界进行粒子分类和数据分析。在这里，我们描述了机器学习在解决 DEAP-3600 暗物质搜索实验（SNOLAB，加拿大）的背景和信号事件分类问题中的应用。我们应用 ML 的 Boosted Decision Trees (BDT) 算法以及 Extra Trees 和 eXtra Gradient Boosting (XGBoost) 方法的改进。