Solar Image Synthesis with Generative Adversarial Networks

Solar activities are caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photo-spheric vector magneto grams of solar active regions have been used to analyze and forecast extreme space weather events such as flares and coronal mass ejections. Unfortunately, the most recent Solar Cycle 24 was relatively weak with few large events, though it is the only solar cycle in which time-series vector magnetograms have been available. In this paper, we focus on two NASA instru-ments, namely the Michelson Doppler Imager (MDI) onboard the Solar and Heliospheric Observatory (SOHO) launched in Solar Cycle 23 (1996–2008), and the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) launched in Solar Cycle 24 (2008–2019). While SOHOIMDI provides data from the more active Solar Cycle 23, it only offers line-of-sight (LOS) magneto grams without vector magnetograms. We propose Solar Image GAN (SIGAN), a generative adversarial network model designed to synthesize vector magnetic field images for Solar Cycles 23 and 24. SIGAN is trained using Hα images, SDOIHMI LOS, and vector magnetograms. It can generate vector magneto grams for both SDOIHMI and SOHOIMDI using Hα images and LOS magneto grams as input. Extensive experiments demonstrated the good performance of the proposed approach.