Journal ArticlePredicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural NetworkLi, Jialiang; Yurchyshyn, Vasyl (ORCID:0000000199822175); Wang, Jason_T L (ORCID:0000000224861097); Wang, Haimin (ORCID:000000025233565X); Georgoulis, Manolis K (ORCID:0000000169131330); He, Wen; Abduallah, Yasser (ORCID:0000000307922270); Farooki, Hameedullah A (ORCID:0000000179528032); Xu, Yan<title>Abstract</title> <p>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 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 hr 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 the literature.</p>IOP Publishing2026-02-1910674737The Astrophysical Journal99823400004-637Xhttps://doi.org/10.3847/1538-4357/ae3c832300341; 2504860National Science Foundation