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Searching for codes credited to 'Maksym Tsizh, Denis Ullmann'

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[submitted] Solar activity classification based on Mg II spectra: towards classification on compressed data

Although large volumes of solar data are available for investigation and study, the vast majority of these data remain unlabeled and
are therefore not amenable to modern supervised machine learning methods. Having a way to accurately and automatically classify
spectra into categories related to the degree of 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. Additionally, the raw data communication imposes
some restrictions on real time data observations and requires considerable bandwidth and energy for the onboard solar observation
systems. To cope with the above mentioned issues, we propose a framework to classify solar activity on compressed data. To this
end, we used a labeling scheme from a pre-existing vector quantization technique in conjunction with several machine learning
algorithms to categorize Mg II spectra measured by NASA’s small explorer satellite IRIS into several groups characterizing solar
activity. Our training data set is a human annotated list of 85 IRIS observations containing 29097 frames in total or equivalently
9 million Mg II spectra. The annotated types of Solar activity are: active region, pre-flare activity, Solar flare, Sunspot and quiet
Sun. We used the vector quantization to compress these data and to reduce its complexity before training classifiers. From a host
of classifiers, we found that the XGBoost classifier produced 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 support vector machines. A principle finding of this research is that the classification performance on compressed
and uncompressed data is comparable under our particular architecture, implying the possibility of large compression rates for
relatively low degrees of information loss.