Machine learning in solar physics

TL;DR

This paper reviews how machine learning techniques, especially deep learning, are transforming solar physics by enabling large-scale data analysis, improving understanding of solar phenomena, and predicting space weather impacts.

Contribution

It highlights recent advances in applying machine learning to solar data analysis, emphasizing new models and automation methods that enhance understanding and prediction of solar activity.

Findings

Deep learning enables analysis of large solar datasets.

Machine learning improves prediction of solar flares.

Automation increases research efficiency.

Abstract

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.