The Supervised Normalized Cut Method for Detecting, Classifying, and Identifying Special Nuclear Materials

Abstract

The detection of illicit nuclear materials is a major tool in preventing and deterring nuclear terrorism. The detection task is extremely difficult because of physical limitations of nuclear radiation detectors, shielding by intervening cargo materials, and the presence of background noise. We aim at enhancing the capabilities of detectors with algorithmic methods specifically tailored for nuclear data. This paper describes a novel graph-theory-based methodology for this task. This research considers for the first time the utilization of supervised normalized cut (SNC) for data mining and classification of measurements obtained from plastic scintillation detectors that are of particularly low resolution. Specifically, the situation considered here is for when both energy spectra and the time dependence of such data are acquired. We present here a computational study, comparing the supervised normalized cut method with alternative classification methods based on support vector machine (SVM), specialized feature-reducing SVMs (i.e., 1-norm SVM, recursive feature elimination SVM, and Newton linear program SVM), and linear discriminant analysis (LDA). The study evaluates the performance of the suggested method in binary and multiple classification problems of nuclear data. The results demonstrate that the new approach is on par or superior in terms of accuracy and much better in computational complexity to SVM (with or without dimension or feature reduction) and LDA with principal components analysis as preprocessing. For binary and multiple classifications, the SNC method is more accurate, more robust, and is computationally more efficient by a factor of 2–80 than the SVM-based and LDA methods.