Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network - Département Métrologie Instrumentation & Information Accéder directement au contenu
Article Dans Une Revue Applied Radiation and Isotopes Année : 2016

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

Résumé

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.
Fichier non déposé

Dates et versions

hal-01849394 , version 1 (26-07-2018)

Identifiants

Citer

C. Bobin, O. Bichler, V. Lourenço, C. Thiam, M. Thevenin. Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network. Applied Radiation and Isotopes, 2016, 109, pp.405 - 409. ⟨10.1016/j.apradiso.2015.12.029⟩. ⟨hal-01849394⟩
133 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More