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

Proceedings of the 20th International Conference on Radionuclide Metrology and its Applications (ICRM), 8-11 June 2015, Vienna, Austria. Eds Franz-Josef Maringer, Dirk Arnold, Uwe WätjenInternational audiencePortal 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 241Am, 133Ba, 207Bi, 60Co, 137Cs) 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