Radioisotope Identification Algorithm Using Deep Artificial Neural Network for Supporting Nuclear Detection and First Response on Nuclear Security Incidents

Abstract

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.