The detection of missing radioactive material is an important capability for safeguards measurements. Gamma ray signatures provide sample information, but interpretation is complicated by measurement environments. To determine if machine learning is a viable analysis option, three algorithms are applied to gamma ray detection data to assess their success at identifying missing sources. Preliminary results demonstrate that these algorithms can predict the number and location of missing sources on simple models of spent fuel assemblies. In addition to simulated experiments, a study to investigate if the algorithms can be trained with simulated data and tested on measured data is presented.

中文翻译：

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用于检测丢失的放射性物质的机器学习算法的实现

检测丢失的放射性物质是保障测量的一项重要能力。伽马射线特征提供样本信息，但解释因测量环境而变得复杂。为了确定机器学习是否是一种可行的分析选项，三种算法被应用于伽马射线探测数据，以评估它们在识别缺失源方面的成功程度。初步结果表明，这些算法可以在乏燃料组件的简单模型上预测缺失源的数量和位置。除了模拟实验之外，还提出了一项研究，以调查是否可以使用模拟数据训练算法并在测量数据上进行测试。