Online ISSN: 1884-4111 Print ISSN: 0033-8303
Radioisotopes 72(2): 121-139 (2023)


核検知及び核セキュリティ事案初動対応を支援する深層ニューラルネットワークを用いた核種判定アルゴリズムRadioisotope Identification Algorithm Using Deep Artificial Neural Network for Supporting Nuclear Detection and First Response on Nuclear Security Incidents

1日本原子力研究開発機構Japan Atomic Energy Agency

2科学警察研究所National Research Institute of Police Science

受付日:2021年6月8日Received: June 8, 2021
受理日:2023年1月24日Accepted: January 24, 2023
発行日:2023年7月15日Published: July 15, 2023


Rapid and precise radioisotope identification in the scene of nuclear detection and nuclear security incidents is one of the challenging issues for the prompt response to the detection alarm or the incidents. A radioisotope identification algorithm using a deep artificial neural network model applicable to handheld γ-ray detectors has been proposed in the present paper. The proposed algorithm automatically identifies gamma-emitting radioisotopes based on the count contribution ratio (CCR) from each of them estimated by the deep artificial neural network model trained by simulated γ-ray spectra. The automated radioisotope identification algorithm can support first responders of nuclear detection and nuclear security incidents without sufficient experience and knowledge in radiation measurement. The authors tested the performance of the proposed algorithm using two different types of deep artificial neural network models in application to handheld detectors having high or low energy resolution. The proposed algorithm showed high performance in identifying artificial radioisotopes for actually measured γ-ray spectra. It was also confirmed that the algorithm is applicable to identifying 235U and automated uranium categorization by analyzing estimated CCRs by the deep artificial neural network models. The authors also compared the performance of the proposed algorithm with a conventional radioisotope identification method and discussed promising ways to improve the performance of the algorithm using the deep artificial neural network.

Key words: nuclear security; radioisotope identification; artificial neural network; γ-ray spectrum; handheld γ-ray detector; Monte Carlo N-Particle (MCNP) code

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