Solar farside magnetograms from deep learning analysis of STEREO/EUVI data

  • Kim, Taeyoung (School of Space Research, Kyung Hee University) ;
  • Park, Eunsu (School of Space Research, Kyung Hee University) ;
  • Lee, Harim (School of Space Research, Kyung Hee University) ;
  • Moon, Yong-Jae (School of Space Research, Kyung Hee University) ;
  • Bae, Sung-Ho (Department of Computer Science and Engineering, College of Electronics and Information, Kyung Hee University) ;
  • Lim, Daye (School of Space Research, Kyung Hee University) ;
  • Jang, Soojeong (Space Science Division, Korea Astronomy and Space Science Institute) ;
  • Kim, Lokwon (Department of Computer Science and Engineering, College of Electronics and Information, Kyung Hee University) ;
  • Cho, Il-Hyun (Department of Astronomy and Space Science, College of Applied Science, Kyung Hee University) ;
  • Choi, Myungjin (InSpace Co., Ltd.) ;
  • Cho, Kyung-Suk (Space Science Division, Korea Astronomy and Space Science Institute)
  • Published : 2019.04.10

Abstract

Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside data2-4, but their quality is lower than that of typical frontside magnetograms. Here we generate farside solar magnetograms from STEREO/Extreme UltraViolet Imager (EUVI) $304-{\AA}$ 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) $304-{\AA}$ 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 $304-{\AA}$ 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.

Keywords

Acknowledgement

Grant : 태양흑점폭발 분석 및 예측기술연구

Supported by : 정보통신기술진흥센터