Solar Physics - Call for papers: "Applications of Machine Learning in Solar Physics"

The journals Solar Physics & Living Reviews in Solar Physics have opened a joined Topical Collection on "Applications of Machine Learning in Solar Physics" (this opens in a new tab), which aims to include review articles and original research. 

This initiative is inspired by a number of recent submissions and the review article by Asensio Ramos et al. (2023): 

"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." [Asensio Ramos et al. (2023) Machine learning in solar physics. Living Rev Sol Phys 20:4]

While the Living Review "Machine learning in solar physics" serves as a general introduction to the topic, we encourage authors to submit their work on specific applications of machine learning methods, neural networks, and deep learning techniques in solar and heliophysics to the journal Solar Physics.