Artificial-intelligence-based Reconstruction of Solar Farside Vector Magnetograms from Multispacecraft Extreme-ultraviolet Data

Abstract

In this study, we generate full-disk vector magnetic field data of the solar farside, as viewed from the Solar Terrestrial Relations Observatory-Ahead (STEREO-A), Solar Terrestrial Relations Observatory-Behind (STEREO-B), and Solar Orbiter (SolO), using a deep learning model based on the Pix2PixCC architecture. Our model takes extreme-ultraviolet (EUV) 304 and 171 A images, together with reference magnetic field data from a surface flux transport (SFT) model, as inputs. To train and evaluate the model, we use EUV images from the Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly and SFT-predicted magnetic field data from one solar rotation earlier as inputs, and we use vector magnetograms from the SDO/Helioseismic and Magnetic Imager (HMI) as targets. For frontside test datasets covering Solar Cycles 24 and 25, our model successfully generates all three vector magnetic field components consistent with those from SDO/HMI, showing improved performance compared with previous studies. We then generate vector magnetic field data by applying the trained model to EUV observations from STEREO-A and SolO. For the first time, we compare the artificial intelligence (AI)-generated results with corresponding SDO/HMI data obtained when STEREO-A and SolO were near inferior conjunction with SDO in 2023 and 2022, respectively. We also track active-region magnetic fields and derive vector magnetic parameters using AI-generated farside data from STEREO-A, STEREO-B, and SolO, along with frontside SDO/HMI data. These results demonstrate the potential for continuous monitoring of solar vector magnetic fields and derived parameters from the farside to the frontside using AI-generated and SDO/HMI data.