Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

C Bobin; O Bichler; V Lourenço; C Thiam; M Thévenin

doi:10.1016/j.apradiso.2015.12.029

Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

Appl Radiat Isot. 2016 Mar:109:405-409. doi: 10.1016/j.apradiso.2015.12.029. Epub 2015 Dec 7.

Authors

C Bobin¹, O Bichler¹, V Lourenço¹, C Thiam¹, M Thévenin²

Affiliations

¹ CEA, LIST, CEA/Saclay, 91191 Gif-sur-Yvette Cedex, France.
² CEA, LIST, CEA/Saclay, 91191 Gif-sur-Yvette Cedex, France. Electronic address: mathieu.thevenin@cea.fr.

PMID: 26706284
DOI: 10.1016/j.apradiso.2015.12.029

Abstract

Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes' rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ((241)Am, (133)Ba, (207)Bi, (60)Co, (137)Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat.

Keywords: Bayesian sequential analysis; Real-time processing; Spiking neural network; γ-Spectrometry.