Classification of radioxenon spectra with deep learning algorithm

In this study, we propose for the first time a model of classification for Beta-Gamma coincidence radioxenon spectra using a deep learning approach through the convolution neural network (CNN) technique. We utilize the entire spectrum of actual data from a noble gas system in Charlottesville (USX75...

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Veröffentlicht in:	Journal of environmental radioactivity 2021-10, Vol.237, p.106718-106718, Article 106718
Hauptverfasser:	Azimi, Sepideh Alsadat, Afarideh, Hossein, Chai, Jong-Seo, Kalinowski, Martin, Gheddou, Abdelhakim, Hofman, Radek
Format:	Artikel
Sprache:	eng
Schlagworte:	Beta-gamma spectra analysis Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) Convolutional neural network (CNN) Deep learning Radioxenon isotopes categorization
Online-Zugang:	Volltext
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