Machine learning in solar physics

• Published in: Living reviews in solar physics 2023-12, Vol.20 (1), p.4-89, Article 4
• Main Authors: Asensio Ramos, Andrés, Cheung, Mark C. M., Chifu, Iulia, Gafeira, Ricardo
• Format: Article
• Language: English
• Subjects: Activity; Astronomy; Astrophysics and Astroparticles; Astrophysics and Cosmology; Atmospheric models; Chromosphere; Corona; Deep learning; Earth environment; Hazardous areas; Machine learning; Methods: data analysis; Photosphere; Physical work; Physics; Physics and Astronomy; Plasma Physics; Review Article; Solar flares; Solar observations; Solar physics; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Sun: general
• ISSN: 1614-4961; 2367-3648; 1614-4961
• DOI: 10.1007/s41116-023-00038-x

The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.