Bobin C, Bichler O, Lourenço V, Thiam C, Thévenin M. Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network.
Appl Radiat Isot 2015;
109:405-409. [PMID:
26706284 DOI:
10.1016/j.apradiso.2015.12.029]
[Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 12/04/2015] [Indexed: 11/29/2022]
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.
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