Artificial intelligence generated solar farside magnetogram using conditional generative adversarial network

Abstract

A solar flare occurs due to a magnetic field reconnection above the active region. The active region magnetic complexity observed in the magnetogram could be used as proxies for solar flare forecasting. It is also known that solar flares that occur from emerging active regions located near the solar disk eastern limb can still have an impact on the Earth. Therefore, magnetic observation of active regions in the solar farside is important to forecast east limb flares occurrences. This study utilizes the conditional Generative Adversarial Network (cGAN) model to generate Artificial Intelligence (AI) solar farside magnetogram. Our model was trained using the solar frontside observations dataset from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) 304 Angstrom as input images and SDO/Helioseismic and Magnetic Imager (HMI) magnetogram as output images. Our model generates solar farside magnetogram using solar farside observation from Solar Terrestrial Relations Observatory (STEREO)/Extreme Ultraviolet Imager (EUVI) 304 Angstrom. We also conducted validation on the similarity of our AI-generated magnetogram with the magnetogram observation from SDO/HMI using the Structural Similarity Index (SSIM) method. SSIM obtained an average similarity value of 0.61±0.06 for training data and 0.47±0.02 for validation data which contain active regions producing flares.