Spectral unmixing applied to fast identification of γ-emitting radionuclides using NaI(Tl) detectors

Spectral unmixing was investigated for fast spectroscopic identification in γ-emitter mixtures at low-statistics in the case of measurements performed to prevent illegal nuclear material trafficking or for in situ environmental analysis following a radiological or nuclear accident. For that purpose,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied radiation and isotopes 2020-04, Vol.158, p.109068-109068, Article 109068
Hauptverfasser: Paradis, H., Bobin, C., Bobin, J., Bouchard, J., Lourenço, V., Thiam, C., André, R., Ferreux, L., de Vismes Ott, A., Thévenin, M.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Spectral unmixing was investigated for fast spectroscopic identification in γ-emitter mixtures at low-statistics in the case of measurements performed to prevent illegal nuclear material trafficking or for in situ environmental analysis following a radiological or nuclear accident. For that purpose, a multiplicative update algorithm based on full-spectrum analysis was tested in the case of a 3″x3″ NaI(Tl) detector. Automatic decision-making was addressed using Monte Carlo calculations of decision thresholds and detection limits. The first results obtained with a portable instrument equipped with a 3″x3″ NaI(Tl) detector designed for the control of food samples by non-expert users following a radiological or nuclear accident, are also presented. •Spectral unmixing based on full-spectrum analysis investigated for fast spectroscopic identification of γ-emitters.•Multiplicative update algorithm developed for automatic decision-making at low-statistics.•Identification code designed to be implemented in embedded digital devices for in situ measurements.•Development of detection systems used to prevent illegal nuclear material trafficking or for environmental analysis.
ISSN:0969-8043
1872-9800
DOI:10.1016/j.apradiso.2020.109068