Improvement of Nuclide Detection through Graph Spectroscopic Analysis Framework and its Application to Nuclear Facility Upset Detection

TL;DR

This paper introduces a neural network-based spectroscopic analysis framework that significantly enhances radionuclide detection sensitivity, demonstrated by a twofold improvement in detecting Cesium releases during nuclear facility upsets.

Contribution

The paper presents a novel neural network approach with an attention mechanism that improves radionuclide detection limits by leveraging temporal and spectral data, surpassing traditional methods.

Findings

Achieved 2x improvement in Cesium detection during nuclear upset scenarios.

Demonstrated the method's potential for more complex decay chains and broader data integration.

Validated the approach's effectiveness over traditional spectroscopic techniques.

Abstract

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.