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We present two recurrent neural networks (RNNs), one based on gated recurrentunits and the other based on long short-term memory, for predicting whether anactive region (AR) that produces an M- or X-class flare will also produce acoronal mass ejection (CME). We model data samples in an AR as time series anduse the RNNs to capture temporal information of the data samples. Each datasample has 18 physical parameters, or features, derived from photosphericvector magnetic field data taken by the Helioseismic and Magnetic Imager (HMI)on board the Solar Dynamics Observatory (SDO). We survey M- and X-class flaresthat occurred from 2010 May to 2019 May using the Geostationary OperationalEnvironmental Satellite's X-ray flare catalogs provided by the National Centersfor Environmental Information (NCEI), and select those flares with identifiedARs in the NCEI catalogs. In addition, we extract the associations of flaresand CMEs from the Space Weather Database Of Notifications, Knowledge,Information (DONKI). We use the information gathered above to build the labels(positive versus negative) of the data samples at hand. Experimental resultsdemonstrate the superiority of our RNNs over closely related machine learningmethods in predicting the labels of the data samples. We also discuss anextension of our approach to predict a probabilistic estimate of how likely anM- or X-class flare will initiate a CME, with good performance results. To ourknowledge this is the first time that RNNs have been used for CME prediction.

Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks