Classification of radioxenon spectra with deep learning algorithm
| Autor: | Sepideh Alsadat Azimi, Hossein Afarideh, Abdelhakim Gheddou, Jong-Seo Chai, Radek Hofman, Martin Kalinowski |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Health Toxicology and Mutagenesis Deep learning Pattern recognition General Medicine Noble gas (data page) Pollution Convolutional neural network Coincidence Spectral line Deep Learning Categorization Air Pollutants Radioactive Radiation Monitoring Environmental Chemistry Review process Neural Networks Computer Artificial intelligence business Waste Management and Disposal Xenon Radioisotopes |
| Zdroj: | Journal of Environmental Radioactivity. 237:106718 |
| ISSN: | 0265-931X |
| DOI: | 10.1016/j.jenvrad.2021.106718 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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