Advancing source reactor-type discrimination using machine learning techniques and SFCOMPO-2.0 experimental database

In recent years, nuclear forensic analysis has become crucial due to the growing global threat of nuclear terrorism and smuggling. Since 2005, extensive research has been conducted on identifying the origin of spent nuclear fuel, focusing on the source reactor-type discrimination, ²³⁵U enrichment of the fresh fuel, and the fuel exposure in the reactor (known as burnup). However, the majority of research relies on computed databases, which may lead to tracing discrepancies compared with actual situations. The present study employs the isotopic measurements from the experimental SFCOMPO-2.0 database to predict nuclear reactor types using Factor Analysis (FA) and various machine learning classification algorithms. The results reveal that FA is an effective method for dimension reduction and visualization. The FA-KNN, Random Forest (RF), and Multilayer Perceptron (MLP) algorithms are applied using a consistent dataset partition to ensure unbiased comparisons. The prediction results based on 10-fold stratified cross-validation are quite promising and the Receiver Operating Characteristic (ROC) curves for multi-class classification confirm the excellent generalization ability of models. Therefore, the application of machine learning techniques is highly effective for reactor-type forensics analysis, especially for RF and MLP.