机器学习驱动的LEGEND数据清洗：一种使用亲和传播和支持向量机的半监督方法
Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines
AI总结：
提出一种基于亲和传播聚类和支持向量机分类的半监督机器学习方法，用于去除LEGEND实验中HPGe探测器捕获的非物理事件，物理事件牺牲率最大仅为0.024%，可加速LEGEND-200和LEGEND-1000的数据清洗。
摘要：
无中微子双β衰变（$0νββ$）是一种稀有核过程，如果被观测到，将为了解中微子的性质提供洞见，并有助于解释宇宙中的物质-反物质不对称性。大型富集锗无中微子双β衰变实验（LEGEND）将分两个阶段运行以搜寻$0νββ$。第一阶段（第二阶段）将采用200（1000）公斤富集$^{76}$Ge的高纯锗（HPGe）探测器，以实现10$^{27}$（10$^{28}$）年的半衰期灵敏度。在本研究中，我们提出了一种由新型人工智能模型驱动的半监督数据驱动方法，以去除HPGe探测器捕获的非物理事件。我们利用亲和传播（Affinity Propagation）根据波形形状对波形信号进行聚类，并使用支持向量机（Support Vector Machine）将其分类为不同类别。我们在北卡罗来纳大学教堂山分校的全链测试实验台上安装的自然丰度HPGe探测器所采集的数据上训练、优化和测试了我们的模型。我们证明，该模型造成的物理事件最大牺牲为$0.024 ^{+0.004}_{-0.003} \%$。我们的模型正被用于加速LEGEND-200的数据清洗开发，并将有助于改进LEGEND-1000的数据清洗程序。
英文摘要：
Neutrinoless double-beta decay ($0νββ$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the universe. The Large Enriched Germanium Experiment for Neutrinoless Double-Beta Decay (LEGEND) will operate in two phases to search for $0νββ$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in $^{76}$Ge to achieve a half-life sensitivity of 10$^{27}$ (10$^{28}$) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize Affinity Propagation to cluster waveform signals based on their shape and a Support Vector Machine to classify them into different categories. We train, optimize, test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.
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。
