Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network

Jialiang Li; Vasyl Yurchyshyn; Jason T. L. Wang; Haimin Wang; Manolis K. Georgoulis; Wen He; Yasser Abduallah; Hameedullah A. Farooki; Yan Xu

arXiv · 2604.10016

Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network

Jialiang Li·Vasyl Yurchyshyn·Jason T. L. Wang·Haimin Wang·Manolis K. Georgoulis·Wen He·Yasser Abduallah·Hameedullah A. Farooki·Yan Xu

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Abstract

Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The association between flares and CMEs is not always obvious. In this study, we propose a new deep learning method, specifically a hybrid neural network (HNN) that combines a vision transformer with long short-term memory, to predict associations between flares and CMEs. HNN finds spatio-temporal patterns in the time series of line-of-sight magnetograms of solar active regions (ARs) collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory and uses the patterns to predict whether a flare projected to occur within the next 24 hours will be eruptive (i.e., CME-associated) or confined (i.e., not CME-associated). Our experimental results demonstrate the good performance of the HNN method. Furthermore, the results show that magnetic flux cancellation in polarity inversion line regions may well play a role in triggering flare-associated CMEs, a finding consistent with literature.

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Jialiang Li, Vasyl Yurchyshyn, Jason T. L. Wang, Haimin Wang, Manolis K. Georgoulis, Wen He, Yasser Abduallah, Hameedullah A. Farooki, Yan Xu. 2026-04-11. Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network. https://arxiv.org/abs/2604.10016

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