用深度学习自动分类太阳活跃区，提升空间天气预测精度。
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
- 对比卷积网络与视觉变换器在太阳活跃区分类中的表现。
- 验证先进训练策略对模型性能的关键作用。
- 适合研究太阳活动预测与机器学习应用的学者。
太阳活跃区可能显著扰乱日地空间环境，常引发太阳耀斑和日冕物质抛射等严重空间天气事件。因此，自动分类活跃区组是实现精准、及时预测太阳活动的关键起点。本研究系统探讨了深度学习技术在基于蒙特威尔逊分类体系的活跃区图像分类中的应用，重点比较了从卷积神经网络到视觉变换器的最新图像分类架构性能，并报告其在活跃区分类任务上的表现。结果表明，模型有效性的关键在于采用领域最新进展的稳健训练流程。
原文摘要 · Abstract (English)
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.
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