Non-negative Matrix Factorization of Gamma-Ray Spectra for Background Modeling, Detection, and Source Identification

Radiological source search is a challenge involving the detection, identification, and localization of weak sources within background environments that vary both spatially and temporally. In this paper, a method for simultaneously detecting and identifying gamma-ray sources using background models formed from spectral data is described. Non-negative matrix factorization (NMF) is used to generate low-dimensional representations of gamma-ray spectra, allowing for a compact means of capturing variation in gamma-ray backgrounds. Background models formed using NMF are used to perform anomaly detection, and additionally, models are augmented with spectral templates of gamma-ray sources to perform simultaneous detection and identification using a likelihood ratio test. The NMF-based detection and identification algorithm is benchmarked against a standard Region of Interest algorithm and shows significant performance gains. In addition, NMF-based anomaly detection shows improvements over methods based on gross counts or principal component analysis. Algorithm performance is evaluated using unshielded sources with activities between 5 and 400 mu Ci at a standoff distance of 20 m using source injection on background data collected using a 1 m(2) NaI array on the Radiological Multisensor Analysis Platform.