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Solar flare prediction plays an important role in understanding andforecasting space weather. The main goal of the Helioseismic and MagneticImager (HMI), one of the instruments on NASA's Solar Dynamics Observatory, isto study the origin of solar variability and characterize the Sun's magneticactivity. HMI provides continuous full-disk observations of the solar vectormagnetic field with high cadence data that lead to reliable predictivecapability; yet, solar flare prediction effort utilizing these data is stilllimited. In this paper, we present a machine-learning-as-a-service (MLaaS)framework, called DeepSun, for predicting solar flares on the Web based onHMI's data products. Specifically, we construct training data by utilizing thephysical parameters provided by the Space-weather HMI Active Region Patches(SHARP) and categorize solar flares into four classes, namely B, C, M, X,according to the X-ray flare catalogs available at the National Centers forEnvironmental Information (NCEI). Thus, the solar flare prediction problem athand is essentially a multi-class (i.e., four-class) classification problem.The DeepSun system employs several machine learning algorithms to tackle thismulti-class prediction problem and provides an application programminginterface (API) for remote programming users. To our knowledge, DeepSun is thefirst MLaaS tool capable of predicting solar flares through the Internet.

DeepSun: Machine-Learning-as-a-Service for Solar Flare Prediction