Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside data2,3,4, but their quality is lower than that of typical frontside magnetograms. Here we generate farside solar magnetograms from STEREO/Extreme UltraViolet Imager (EUVI) 304-Å images using a deep learning model based on conditional generative adversarial networks (cGANs). We train the model using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) 304-Å images and SDO/Helioseismic and Magnetic Imager (HMI) magnetograms taken from 2011 to 2017 except for September and October each year. We evaluate the model by comparing pairs of SDO/HMI magnetograms and cGAN-generated magnetograms in September and October. Our method successfully generates frontside solar magnetograms from SDO/AIA 304-Å images and these are similar to those of the SDO/HMI, with Hale-patterned active regions being well replicated. Thus we can monitor the temporal evolution of magnetic fields from the farside to the frontside of the Sun using SDO/HMI and farside magnetograms generated by our model when farside extreme-ultraviolet data are available. This study presents an application of image-to-image translation based on cGANs to scientific data.
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The code is available at https://github.com/tykimos/SolarMagGAN. In the readme file, we explain the architecture and selected hyperparameters. The SDO data are available from the SDO data centre (https://sdo.gsfc.nasa.gov/data/), the Joint Science Operations Center (http://jsoc.stanford.edu/) and the Korean Data Center for SDO (http://sdo.kasi.re.kr/). The STEREO data are available from the STEREO Science Center (https://stereo-ssc.nascom.nasa.gov/data.shtml). We used the following open-source packages: NumPy (http://www.numpy.org) and Keras (https://keras.io/).
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We thank the numerous team members who contributed to the success of the SDO mission, as well as the STEREO mission. We acknowledge the community efforts devoted to developing the open-source packages that were used in this work (NumPy and Keras). This work was supported by the BK21+ Program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea, the Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2016R1A2B4013131), a grant from the NRF funded by the Korean government (number NRF-2013M1A3A3A02042232), the Korea Astronomy and Space Science Institute under the R&D programme supervised by the Ministry of Science and ICT, the Korea Astronomy and Space Science Institute under the R&D programme ‘Development of a Solar Coronagraph on International Space Station (Project No. 2019-1-850-02)’ supervised by the Ministry of Science and ICT, a grant from the Institute for Information & Communications Technology Promotion (IITP) funded by the Korea government (MSIP) (number 2018-0-01422, ‘Study on Analysis and Prediction Technique of Solar Flares’), and the Artificial Intelligence Laboratory at the InSpace Co., Ltd. The SDO data were (partly) provided by the Korea Data Center (KDC) for SDO in cooperation with NASA and the SDO/HMI Team, which is operated by the KASI.