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 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.