Clandestine nuclear testing can be detected at a standoff distance using radioxenon beta-gamma analysis. International treaty monitoring organizations depend, in part, upon the activity ratios of various radioxenon types to determine if collected samples are the result of a weapons test or a peaceful purpose such as energy or medical isotope production. However, the currently deployed radioxenon analysis method makes assumptions about the location of energy coincidence counts on a beta-gamma spectrum, such that this method is particularly sensitive to measurement or calibration errors. We propose a machine learning method instead. By exposing a computer algorithm to many representative examples, the resultant computer model detects patterns in the data without making additional assumptions. Both a classification model predicting which radioisotopes are present and a regression model predicting concentrations of the radioisotopes are tested. This work is a proof-of-concept that machine learning can be effectively applied to radioxenon beta-gamma analysis.

中文翻译：

---

用于分析 2D 放射性氙 β γ 谱的机器学习

可以使用放射性氙 β-γ 分析在一定距离处检测到秘密核试验。国际条约监测组织部分依赖于各种放射性氙的活度比来确定收集的样本是武器试验的结果还是和平目的（例如能源或医疗同位素生产）的结果。然而，当前部署的放射性氙分析方法对β-伽马光谱上能量重合计数的位置进行了假设，因此该方法对测量或校准误差特别敏感。我们提出了一种机器学习方法。通过将计算机算法暴露于许多具有代表性的示例，生成的计算机模型无需做出额外假设即可检测数据中的模式。测试了预测存在哪些放射性同位素的分类模型和预测放射性同位素浓度的回归模型。这项工作证明了机器学习可以有效地应用于放射性氙 β-γ 分析。