学术文献库

Although large volumes of solar data are available for study, the vast majority of these data remain unlabeled and are therefore not amenable to supervised machine learning methods. Having a way to accurately and automatically classify spectra into categories related to solar activity is highly desirable and will assist and speed up future research efforts in solar physics. At the same time, the large volume of raw observational data is a serious bottleneck for machine learning, requiring powerful computational means that are not at the disposal of many laboratories. Besides, the raw data communication imposes restrictions on real time data observations and requires considerable bandwidth and energy for the onboard solar observation systems. To solve these issues, we propose a framework to classify solar activity on compressed data. For this, we used a labeling scheme from a pre-existing vector quantization technique in conjunction with different machine learning algorithms to categorize spectra of singly-ionized magnesium Mg II measured by NASA's Interface Region Imaging Spectrograph satellite (IRIS) into five types of solar activity. Our training dataset is a human annotated list of 85 IRIS observations containing 29097 frames. The annotated types of Solar activities are active region, pre-flare activity, Solar flare, Sunspot, and quiet Sun. We compress these data and reduce its complexity before training classifiers. We found that the XGBoost classifier produces the most accurate results on the compressed data, yielding over a 95\% prediction rate, and outperforming other ML methods like convolution neural networks, K-nearest neighbors, naive Bayes classifiers, and SVM. We find that the classification performance on compressed and uncompressed data is comparable, implying the possibility of large compression rates for relatively low degrees of information loss.