Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Matters Arising
  • Published:

Reply to: Reliability of AI-generated magnetograms from only EUV images

The Original Article was published on 12 February 2021

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

References

  1. Liu, J. et al. Reliability of AI-generated magnetograms from only EUV images. Nat. Astron. https://doi.org/10.1038/s41550-021-01310-6 (2021).

  2. Kim, T. et al. Solar farside magnetograms from deep learning analysis of STEREO/EUVI data. Nat. Astron. 3, 397–400 (2019).

    Article  ADS  Google Scholar 

  3. Wang, T.-C. et al. High-resolution images synthesis and semantic manipulation with conditional GANs. IEEE Proc. Comput. Vis. Pattern Recog. 2018, 8798–8807 (2018).

    Google Scholar 

  4. Shin, G. et al. Generation of high-resolution solar pseudo-magnetograms from Ca ii K images by deep learning. Astrophys. J. Lett. 895, L16 (2020).

    Article  ADS  Google Scholar 

  5. Jeong, H.-J., Moon, Y.-J., Park, E. & Lee, H. Solar coronal magnetic field extrapolation from synchronic data with AI-generated farside. Astrophys. J. Lett. 903, L25 (2020).

    Article  ADS  Google Scholar 

  6. Lindsey, C. & Braun, D. C. Seismic images of the far side of the Sun. Science 287, 1799–1801 (2000).

    Article  ADS  Google Scholar 

  7. Park, E. et al. Generation of solar UV and EUV images from SDO/HMI magnetograms by deep learning. Astrophys. J. Lett. 884, L23 (2019).

    Article  ADS  Google Scholar 

  8. Park, E., Moon, Y.-J., Lim, D. & Lee, H. De-noising SDO/HMI solar magnetograms by image translation method based on deep learning. Astrophys. J. Lett. 891, L4 (2020).

    Article  ADS  Google Scholar 

  9. Ji, E.-Y., Moon, Y.-J. & Park, E. Improvement of IRI global TEC maps by deep learning based on conditional generative adversarial networks. Space Weather 18, e2019SW002411 (2020).

    Article  ADS  Google Scholar 

  10. Arge, C. N. & Henney, C. J. Modeling the corona and solar wind using ADAPT maps that include far-side observations. AIP Conf. Proc. 1539, 11–14 (2013).

    Article  ADS  Google Scholar 

  11. Freeland, S. & Handy, B. Data analysis with the SolarSoft system. Sol. Phys. 182, 497–500 (1998).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Basic Science Research Program through the National Research Foundation (NRF) funded by the Ministry of Education (NRF-2019R1A2C1002634, NRF-2019R1C1C1004778 and NRF-2020R1C1C1003892), the Korea Astronomy and Space Science Institute (KASI) under the R&D program ‘Study on the Determination of Coronal Physical Quantities using Solar Multi-wavelength Images (project number 2019-1-850-02)’ supervised by the Ministry of Science and ICT and an Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (2018-0-01422, Study on analysis and prediction technique of solar flares).

Author information

Authors and Affiliations

Authors

Contributions

E.P., H.-J.J., H.L., T.K. and Y.-J.M. wrote the manuscript.

Corresponding author

Correspondence to Yong-Jae Moon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Peer review information Nature Astronomy thanks Nick Arge and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, E., Jeong, HJ., Lee, H. et al. Reply to: Reliability of AI-generated magnetograms from only EUV images. Nat Astron 5, 111–112 (2021). https://doi.org/10.1038/s41550-021-01311-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41550-021-01311-5

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics