The application of machine learning in solar physics

Abstract

Solar physics has entered the era of big data, and machine learning has gained more and more recognition as a good tool for big data research. This paper reviews the application results of machine learning in solar physics since 2007. Our studies have shown that research in this field has increased significantly during the last four years. Massive solar observation data obtained from various instruments on the ground and in space have been applied, and the topics have covered major aspects of solar physics, such as solar flares, coronal mass ejections, sunspots. Although some good results have emerged and proved that machine learning is suitable for data analysis of solar physics, there has not been a breakthrough yet. The machines learning methods that used in this field involve classification, regression, clustering, dimensionality reduction, and deep learning. However, classical algorithms, especially classical classification method is more popular. This means that the application of machine learning in solar physics is still in its infancy, but it also means that there is still a lot of work in this field that can be studied in the future.