论文标题
在DEAP-3600实验中提高决策树的颈部Alpha事件歧视方法
Boosted decision trees approach to neck alpha events discrimination in DEAP-3600 experiment
论文作者
论文摘要
机器学习（ML）已被广泛应用于高能物理学，以帮助物理社区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来解决DEAP-3600暗物质搜索实验（加拿大Snolab）的背景和信号事件的问题。我们应用了ML的增强决策树（BDT）算法，并改善了额外的树木和额外的梯度增强（XGBoost）方法。
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.
