Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

Abstract

Artificial neural networks (ANNs) for performing spectroscopic gamma-ray source identification have been previously introduced, primarily for applications in controlled laboratory settings. To understand the utility of these methods in scenarios and environments more relevant to nuclear safety and security, this work examines the use of ANNs for mobile detection, which involves highly variable gamma-ray background, low signal-to-noise ratio measurements, and low false alarm rates. Simulated data from a 2” × 4” × 16” NaI(Tl) detector are used in this work for demonstrating these concepts, and the minimum detectable activity (MDA) is used as a performance metric in assessing model performance.In addition to examining simultaneous detection and identification, binary spectral anomaly detection using autoencoders is introduced in this work, and benchmarked using detection methods based on Non-negative Matrix Factorization (NMF) and Principal Component Analysis (PCA). On average, the autoencoder provides a 12% and 23% improvement over NMF- and PCA-based detection methods, respectively. Additionally, source identification using ANNs is extended to leverage temporal dynamics by means of recurrent neural networks, and these time-dependent models outperform their time-independent counterparts by 17% for the analysis examined here. The paper concludes with a discussion on tradeoffs between the ANN-based approaches and the benchmark methods examined here.

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

“…Table I shows the list of source types and activities used for the dataset. The same dataset was also used for benchmarking by Bilton et al, and more detailed data generation procedure is described in [14]. The algorithms were assessed on the augmented dataset in terms of receiver operating characteristic (ROC) curves and minimum detectable amount (MDA).…”

“…Others make use of specific energy windows within which the source and background count contributions are estimated using the spectrum outside the windows [13]. Additionally, some recent works utilize modern machine learning techniques, such as deep neural networks (DNN) [14], [15] and Gaussian processes (GP) [16].…”