水切伦科夫探测器中的伽马中子放射源识别
Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors
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中文总结 AI 辅助
本研究在水切伦科夫探测器中，结合统计分析与软投票集成机器学习模型，实现伽马中子放射源的有效 discrimination，提升了辐射识别能力，可应用于核安全领域。
中文摘要 AI 辅助
水切伦科夫探测器（WCDs）是一种成熟技术，广泛应用于天体物理学、高能物理学领域，近年也用于核安全应用。它通过探测在水中运动速度超过光速的带电粒子产生的切伦科夫光来识别高能相互作用。本研究展示了在WCDs中实现伽马与中子 discrimination 的可行性，采用结合统计分析与机器学习技术的组合方法。实验装置使用不同屏蔽配置，以分离²⁴¹AmBe源的伽马和中子贡献，同时使用⁶⁰Co和¹³⁷Cs源建立信号与能量校准。基于3σ显著性准则的统计分析用于定义能量阈值，使测量的电荷谱与沉积能量形成线性关系。在此校准基础上，进一步通过机器学习方法利用脉冲形状信息改进事件分类。基于软投票策略的集成模型，结合Bagging分类器、CatBoost和多层感知机，在不同屏蔽条件下采集的探测器信号上进行训练，达到0.816的准确率和受试者工作特征（ROC）曲线下面积。该组合方法表明，统计阈值法在全能量范围内提供了具有物理依据的 discrimination 基线，而机器学习通过利用脉冲级信息提升了高能区的分类性能。这种集成策略增强了水切伦科夫探测器的辐射识别能力，在核安全和辐射探测领域具有潜在应用。
英文摘要
Water Cherenkov Detectors (WCDs) are a robust technology widely used in astrophysics, high energy physics, and recently nuclear security applications. They detect high energy interactions through the Cherenkov light emitted by charged particles traveling faster than the speed of light in water. In this work, we demonstrate the feasibility of gamma-neutron discrimination in WCDs using a combined methodology that integrates statistical analysis with machine learning techniques. The experimental setup employs different shielding configurations to isolate gamma and neutron contributions from a \textsuperscript{241}AmBe source, while \textsuperscript{60}Co and \textsuperscript{137}Cs sources are used to establish a signal to energy calibration. A statistical analysis based on a $3σ$ significance criterion is used to define energy thresholds, enabling a linear relationship between the measured charge spectrum and the deposited energy. Building on this calibration, pulse shape information is further exploited through machine learning methods to improve event classification. An ensemble model based on a soft-voting strategy combining a Bagging classifier, CatBoost, and a Multilayer Perceptron was trained on detector signals acquired under different shielding conditions, achieving an accuracy of 0.816 and an area under the Receiver Operating Characteristic (ROC) curve. The combined approach demonstrates that statistical thresholding provides a physically grounded discrimination baseline across the full energy range, while machine learning enhances classification performance at higher energies by leveraging pulse level information. This integrated strategy improves radiation identification capabilities in water Cherenkov detectors, with potential applications in nuclear security and radiation detection.
