Bobin, C.; Bichler, O.; Lourenço, V.; Thiam, C. & Thevenin, M.
Real-time radionuclide identification in gamma-emitter mixtures based on spiking neural network
Applied Radiation and Isotopes, 2016, 109, 405 – 409

http://dx.doi.org/10.1016/j.apradiso.2015.12.029

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 (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.