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

Abstract

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.

9 figures, 1 table

References in corpus (8)

• A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
• Predicting Solar Flares Using a Long Short-Term Memory Network
• A Hybrid Ensemble method for Pulsar Candidate Classification
• Flaring Rates and the Evolution of Sunspot Group McIntosh Classifications
• Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning
• Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
• Mimicking spectropolarimetric inversions using convolutional neural networks
• RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks

Cited by in corpus (3)

• Data mining techniques on astronomical spectra data. II : Classification Analysis
• Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
• Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning