基于提升决策树的DEAP-3600实验中颈α事件识别
Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment
摘要：
机器学习在高能物理领域有着广泛的应用，可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的应用，以解决分类的背景和信号事件的DEAP-3600暗物质搜索实验（SNOLAB，加拿大）的问题。我们应用了ML的Boosted Decision Trees（BDT）算法，并对Extra Trees和Extra Gradient Boosting（XGBoost）方法进行了改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
