Deep Learning Techniques for Sunspot Classification

A solar active region disrupts the Sun–Earth space environment, often leading to severe space weather events. Sunspots are the primary markers of these active regions, and specific sunspot types are associated with space weather events such as solar flares and coronal mass ejections. Therefore, the automatic classification of sunspot groups is beneficial for accurately and promptly predicting solar activity. A significant challenge in this problem is the inherent imbalance in the dataset of solar active regions. The distribution of sunspot types is highly skewed, with more complex sunspot structures being much rarer than simpler ones. This imbalance can lead to biased models that underperform in identifying the less common, yet potentially more critical, sunspot types associated with severe space weather events. In this talk, we will present our results in applying deep learning techniques to the classification of sunspot magnetogram cutouts based on the Mount Wilson classification scheme. We will review the current state-of-the-art architectures in image classification, from Convolutional Neural Networks to Vision Transformers, and present the best network architectures for this task as well as our training pipeline. In addition, we will discuss the most effective techniques for dealing with the strong dataset imbalance, including data augmentation and appropriate choices of loss functions. This work has been supported by the Active Region Classification and Flare Forecasting (ARCAFF) project.