用注意力神经网络提升核素检测灵敏度，可翻倍改善误报率。
Improvement of Nuclide Detection through Graph Spectroscopic Analysis Framework and its Application to Nuclear Facility Upset Detection
- 引入时间与光谱联合分析，通过注意力机制动态调整检测阈值。
- 对铯泄漏检测灵敏度提升2倍，显著优于传统方法。
- 适用于复杂衰变链核素，且可扩展融合脉冲质量等多维数据。
我们提出一种基于图谱分析框架的辐射探测方法，利用光谱辐射探测器及每个辐射量子的到达时间，通过带有注意力机制的神经网络实现。该方法在核设施异常事件中检测铯释放时，相比传统光谱方法提升了2倍的检测灵敏度。我们推测其性能提升源于根据预期探测率调节检测概率，具体表现为依据时间事件分布和局部光谱特征动态调整检测阈值，并提供了相应证据。该方法具有广泛适用性，可能在更复杂的衰变链核素检测中表现更优；此外，该框架不仅限于加入到达时间，还可整合每条探测事件的脉冲质量、探测器位置信息，甚至跨探测器的能量-时间联合分析。
原文摘要 · Abstract (English)
We present a method to improve the detection limit for radionuclides using spectroscopic radiation detectors and the arrival time of each detected radiation quantum. We enable this method using a neural network with an attention mechanism. We illustrate the method on the detection of Cesium release from a nuclear facility during an upset, and our method shows $2\times$ improvement over the traditional spectroscopic method. We hypothesize that our method achieves this performance increase by modulating its detection probability by the overall rate of probable detections, specifically by adapting detection thresholds based on temporal event distributions and local spectral features, and show evidence to this effect. We believe this method is applicable broadly and may be more successful for radionuclides with more complicated decay chains than Cesium; we also note that our method can generalize beyond the addition of arrival time and could integrate other data about each detection event, such as pulse quality, location in detector, or even combining the energy and time from detections in different detectors.
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