Deep-learning-based Identification of Solar Magnetic Tornadoes and Their Spatial Properties during Solar Minimum and Maximum

Abstract Solar magnetic tornadoes are dynamic, spiral-shaped plasma structures characterized by helical magnetic fields and rotating plasma flows in the solar atmosphere. They play a significant role in the transport of energy and mass within the solar environment. Identifying and analyzing solar magnetic tornadoes is challenging due to their transient nature and complex morphology and the large volume of associated observational data. We propose two automated methods for detecting these magnetoplasma structures using modern deep learning techniques. Our models search for twisted prominences in the solar corona visible at the solar limb. Our approach involves analyzing the Solar Dynamics Observatory Atmospheric Imaging Assembly 171 Å images using convolutional and recurrent neural networks. By applying established techniques, the methods proposed can detect previously unknown magnetic tornadoes alongside those documented in the literature. The models are trained on 10,294 instances, which corresponds to detection of ∼100 tornadoes with high precision and recall. Identification of 1,476,885 new instances is performed. The resulting database allows for the first comparative analysis of magnetic tornadoes’ spatial distributions across solar cycle phases. We find that tornadoes can serve as tracers of environments prone to reconnection. During solar minimum, these structures occur at the boundaries of coronal holes with strong current sheets and at the edges of polar conic current sheets. During solar maximum, they appear at the footpoints of magnetic loops and are associated with polarity inversion lines.