In radioactive source surveying protocols, a number of task-inherent features degrade the quality of collected gamma ray spectra, including: limited dwell times, a fluctuating background, a large distance to the source, weak source activity, and the low sensitivity of mobile detectors. Thus, collected gamma ray spectra are expected to be sparse and noise dominated. For extremely sparse spectra, direct background subtraction is infeasible and many background estimation techniques do not apply. In this paper, we present a statistical algorithm for source estimation and anomaly detection under such conditions. We employ a fixed-hyperparameter Gaussian processes regression methodology with a linear innovation sequence scheme in order to quickly update an ongoing source distribution estimate with no prior training required. We have evaluated the effectiveness of this approach for anomaly detection using background spectra collected with a Kromek D3S and simulated source spectrum and hyperparameters defined by detector characteristics and information derived from collected spectra. We attained an area under the ROC curve of 0.902 for identifying sparse source peaks within a sparse gamma ray spectrum and achieved a true positive rate of 93% when selecting the optimum thresholding value derived from the ROC curve.

中文翻译：

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基于核的高斯过程，用于稀疏伽玛射线数据中的异常检测。

在放射源调查协议中，许多任务固有的功能会降低所收集的伽马射线谱的质量，包括：停留时间有限，背景波动，与放射源的距离较大，放射源活动弱以及移动探测器的灵敏度低。因此，预期收集的伽马射线谱将稀疏且以噪声为主。对于极为稀疏的光谱，直接进行背景减法是不可行的，因此许多背景估计技术都不适用。在本文中，我们提出了一种统计算法，用于在这种条件下进行源估计和异常检测。我们采用具有线性创新序列方案的固定超参数高斯过程回归方法，以快速更新正在进行的源分布估计，而无需事先培训。我们使用Kromek D3S收集的背景光谱以及由检测器特性定义的模拟源光谱和超参数以及从收集的光谱中得出的信息，评估了这种方法对异常检测的有效性。我们在ROC曲线下获得了0.902的面积，用于识别稀疏伽马射线谱内的稀疏源峰，并且在选择从ROC曲线得出的最佳阈值时达到了93％的真实阳性率。