Applying Machine Learning Algorithms to Classify Digitized Special Nuclear Material Obtained from Scintillation Detectors

The capability to discriminate among nuclear fuel properties is essential for a successful nuclear safeguard and security program. Accurate nuclear material identification is hindered due to challenges such as differing levels of enrichments, weak radiation signals in the case of fresh nuclear fuel, and complex self-shielding effects. This study explores the application of supervised machine learning algorithms to digitized radiation detector data for classifying signatures of special nuclear materials. Three scintillation detectors, an EJ-309 liquid scintillator, a CLYC crystal scintillator, and an EJ-276 plastic scintillator, were used to measure gamma-ray and neutron data from special nuclear material at the National Criticality Experiments Research Center (NCERC) at the National Nuclear Security Site (NNSS), at Nevada, USA. Radiation detector pulse data was extracted from the collected digitized data and applied to three separate supervised learning models: Random Forest, XGBoost, and a feedforward Deep Neural Network, chosen for their wide-spread use and distinct data ingest and processing analytics. Through model refinement, such as adding an additional parameter feature, an accuracy of greater than 95% was achieved. Analysis on model parameter feature importance revealed Countrate, which is the overall gamma-ray and neutron incidents for each detector, was the most influential parameter and essential to include for improved classification. Initial model versions not including the Countrate parameter feature failed to classify. Supervised learning models allow for measured gamma-ray and neutron pulse data to be used to develop effective identification and discrimination between material compositions of different fuel assemblies. The study demonstrated that traditional pulse shape parameters alone were insufficient for discriminating between special nuclear materials; the addition of Countrate markedly improved model accuracy but all models were heavily dependent on this specific feature, thus illustrating the need for alternative, more distinct parameter features. The machine learning development framework captured in this study will be beneficial for future applications in discriminating between different fuel enrichments and additives such as burnable poisons.