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Article

Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection

1
Department of Nuclear Engineering at the University of California, Berkeley, Berkeley, CA 94720, USA
2
Applied Nuclear Physics Program at Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Bethany L. Goldblum and Thibault Laplace
J. Nucl. Eng. 2021, 2(2), 190-206; https://doi.org/10.3390/jne2020018
Received: 18 February 2021 / Revised: 20 April 2021 / Accepted: 6 May 2021 / Published: 17 May 2021
(This article belongs to the Special Issue Nuclear Security and Nonproliferation Research and Development)
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. View Full-Text
Keywords: gamma-ray source identification; gamma-ray spectroscopy; neural networks; machine learning; classification gamma-ray source identification; gamma-ray spectroscopy; neural networks; machine learning; classification
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MDPI and ACS Style

Bilton, K.J.; Joshi, T.H.Y.; Bandstra, M.S.; Curtis, J.C.; Hellfeld, D.; Vetter, K. Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection. J. Nucl. Eng. 2021, 2, 190-206. https://doi.org/10.3390/jne2020018

AMA Style

Bilton KJ, Joshi THY, Bandstra MS, Curtis JC, Hellfeld D, Vetter K. Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection. Journal of Nuclear Engineering. 2021; 2(2):190-206. https://doi.org/10.3390/jne2020018

Chicago/Turabian Style

Bilton, Kyle J., Tenzing H. Y. Joshi, Mark S. Bandstra, Joseph C. Curtis, Daniel Hellfeld, and Kai Vetter. 2021. "Neural Network Approaches for Mobile Spectroscopic Gamma-Ray Source Detection" Journal of Nuclear Engineering 2, no. 2: 190-206. https://doi.org/10.3390/jne2020018

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