Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers

TL;DR

This paper systematically evaluates deep learning models, including CNNs and Vision Transformers, for classifying solar active regions, emphasizing the importance of robust training for accurate space weather prediction.

Contribution

It provides a comprehensive comparison of modern deep learning architectures applied to solar active region classification, highlighting the impact of training strategies.

Findings

Vision Transformers outperform CNNs in classification accuracy.

Robust training methods significantly improve model performance.

Deep learning models can effectively classify active regions based on Mount Wilson scheme.

Abstract

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.