Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
This work addresses the need for accurate and prompt solar activity prediction for space weather forecasting, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of automatically classifying solar active regions to predict space weather events by applying deep learning techniques, achieving competitive performance with various architectures including CNNs and Vision Transformers.
A solar active region can significantly disrupt the Sun Earth space environment, often leading to severe space weather events such as solar flares and coronal mass ejections. As a consequence, the automatic classification of active region groups is the crucial starting point for accurately and promptly predicting solar activity. This study presents our results concerned with the application of deep learning techniques to the classification of active region cutouts based on the Mount Wilson classification scheme. Specifically, we have explored the latest advancements in image classification architectures, from Convolutional Neural Networks to Vision Transformers, and reported on their performances for the active region classification task, showing that the crucial point for their effectiveness consists in a robust training process based on the latest advances in the field.