Classification of radioxenon spectra with deep learning algorithm. (October 2021)

• Record Type: Journal Article
• Authors: Azimi, Sepideh Alsadat; Afarideh, Hossein; Chai, Jong-Seo; Kalinowski, Martin; Gheddou, Abdelhakim; Hofman, Radek
• Is Part Of: Journal of environmental radioactivity. Volume 237(2021)
• Publication Date: 2021-10
• Subjects: Radioxenon isotopes categorization -- Beta-gamma spectra analysis -- Deep learning -- Convolutional neural network (CNN) -- Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO); Radioactivity -- Periodicals; Radiation, Background -- Periodicals; Radioecology -- Periodicals; Radioactive pollution -- Periodicals; Environmental Pollutants -- Periodicals; Radioactive Pollutants -- Periodicals; Radioactivity -- Periodicals; Radioécologie -- Périodiques; Pollution radioactive -- Périodiques; Fond de rayonnement -- Périodiques; 539.752
• DOI: 10.1016/j.jenvrad.2021.106718
• Languages: English
• ISSNs: 0265-931X

Abstract

Abstract: 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 station) between 2012 and 2019. This study shows that the deep learning categorization can be done as an important pre-screening method without directly involving critical limits and abnormal thresholds. Our results demonstrate that the proposed approach of combining nuclear engineering and deep learning is a promising tool for assisting experts in accelerating and optimizing the review process of clean background and CTBT-relevant samples with high classification average accuracies of 92% and 98%, respectively. Highlights: Deep learning for Beta-Gamma coincidence radioxenon spectra classification. Screening samples that are not interesting in the CTBT context. Without making use of the screening threshold values and background spectra. Noble gas classification by CNN technique as prescreening for CTBT relevant samples.