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  • Matters Arising
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Reliability of AI-generated magnetograms from only EUV images

Nature Astronomy volume 5, pages 108–110 (2021)Cite this article

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Matters Arising to this article was published on 12 February 2021

The Original Article was published on 04 March 2019

arising from T. Kim et al. Nature Astronomy https://doi.org/10.1038/s41550-019-0711-5 (2019)

Kim et al.1 proposed an artificial intelligence (AI) model to predict the photospheric magnetograms of the Sun using extreme ultraviolet (EUV) observations as the only inputs, and concluded that their model is “reliable if the farside active regions conform to Hale’s law, as long as the slight overestimation of their total flux and a possible slight difference in their tilt angle are considered”. In this Matters Arising, we present a detailed sensitivity study of the AI algorithm used by Kim et al.1. Despite identifying issues in the data preparation process and the possibility of data leakage in their work1, we also found the physics basis of this idea problematic. We detail our concerns and analysis below, as well as in the Supplementary Information.

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Fig. 1: Example of the observations and the AI-generated magnetograms.

Data availability

SDO/AIA and SDO/HMI data are publicly available from NASA’s SDO website (https://sdo.gsfc.nasa.gov/data/). Details of the dataset we used are available at https://github.com/yiminking/pix2pix_EUV2HMI_datasets. Source data are provided with this paper.

Code availability

Codes for the AI models built in this paper are available at https://github.com/tykimos/SolarMagGAN. Codes used for the detection of active regions are available upon request from the corresponding author.

References

  1. 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 

  2. Camporeale, E. The challenge of machine learning in space weather: nowcasting and forecasting. Space Weather 17, 1166–1207 (2019).

    Article  ADS  Google Scholar 

  3. Wang, H. N., Cui, Y. M., Li, R., Zhang, L. Y. & Han, H. Solar flare forecasting model supported with artificial neural network techniques. Adv. Space Res. 42, 1464–1468 (2008).

    Article  ADS  Google Scholar 

  4. Bobra, M. G. & Couvidat, S. Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. Astrophys. J. 798, 135–145 (2015).

    Article  ADS  Google Scholar 

  5. Korsós, M. B., Ludmány, A., Erdélyi, R. & Baranyi, T. On flare predictability based on sunspot group evolution. Astrophys. J. Lett. 802, L21–L26 (2015).

    Article  ADS  Google Scholar 

  6. Nishizuka, N. et al. Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. Astrophys. J. 835, 156–165 (2017).

    Article  ADS  Google Scholar 

  7. Liu, C., Deng, N., Wang, J. T. L. & Wang, H. Predicting solar flares using SDO/HMI vector magnetic data products and the random forest algorithm. Astrophys. J. 843, 104–117 (2017).

    Article  ADS  Google Scholar 

  8. Florios, K. et al. Forecasting solar flares using magnetogram-based predictors and machine learning. Sol. Phys. 293, 28–69 (2018).

    Article  ADS  Google Scholar 

  9. Huang, X. et al. Deep learning based solar flare forecasting model. I. Results for line-of-sight magnetograms. Astrophys. J. 856, 7–17 (2018).

    Article  ADS  Google Scholar 

  10. Liu, J., Ye, Y., Shen, C., Wang, Y. & Erdélyi, R. A new tool for CME arrival time prediction using machine learning algorithms: CAT-PUMA. Astrophys. J. 855, 109–118 (2018).

    Article  ADS  Google Scholar 

  11. Wang, Y., Liu, J., Jiang, Y. & Erdélyi, R. CME arrival time prediction using convolutional neural network. Astrophys. J. 881, 15–24 (2019).

    Article  ADS  Google Scholar 

  12. Lemen, J. R. et al. The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Sol. Phys. 275, 17–40 (2012).

    Article  ADS  Google Scholar 

  13. Scherrer, P. et al. The Helioseismic and Magnetic Imager (HMI) investigation for the Solar Dynamics Observatory (SDO). Sol. Phys. 275, 207–227 (2012).

    Article  ADS  Google Scholar 

  14. Arge, C. N. et al. in Solar Wind 13 (eds Zank, G. P. et al.) 11–14 (American Institute of Physics, 2013).

  15. Wang, Y. & Zhang, J. A statistical study of solar active regions that produce extremely fast coronal mass ejections. Astrophys. J. 680, 1516–1522 (2008).

    Article  ADS  Google Scholar 

  16. Zhang, J., Wang, Y. & Liu, Y. Statistical properties of solar active regions obtained from an automatic detection system and the computational biases. Astrophys. J. Lett. 723, 1006–1018 (2010).

    Article  ADS  Google Scholar 

Download references

Acknowledgements

We acknowledge the use of the data from the SDO, which is the first mission for the NASA’s Living With a Star (LWS) programme. J.L. and R.E. thank the STFC (UK, grant number ST/M000826/1) and EU H2020 (SOLARNET grant number 158538) for funding. J.L. also acknowledges support from the STFC under grant number ST/P000304/1 and from the Leverhulme Trust via grant number RPG-2019-371. R.E. also acknowledges the support from the Chinese Academy of Sciences President’s International Fellowship Initiative (PIFI, grant number 2019VMA0052) and The Royal Society (grant number IE161153). Yimin Wang thanks the Solar Physics and Space Plasma Research Centre (SP2RC), School of Mathematics and Statistics (SoMaS) at the University of Sheffield for the warm hospitality and support received as an MSRC Visiting Research Fellow while carrying out this research. M.B.K. thanks the STFC for support under grant number ST/S000518/1. X.H. acknowledges the support from the National Natural Science Foundation of China (grant number 11873060).

Author information

Author notes

  1. These authors contributed equally: Jiajia Liu, Yimin Wang.

Authors and Affiliations

  1. Solar Physics and Space Plasma Research Centre (SP2RC), School of Mathematics and Statistics, University of Sheffield, Sheffield, UK

    Jiajia Liu, Yimin Wang & Robert Erdélyi

  2. Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University, Belfast, UK

    Jiajia Liu

  3. School of Electrical Engineering, University of Jinan, Jinan, China

    Yimin Wang

  4. Key Laboratory of Solar Activity, National Astronomical Observatories of Chinese Academy of Sciences, Beijing, China

    Xin Huang

  5. Department of Physics, Aberystwyth University, Aberystwyth, UK

    Marianna B. Korsós

  6. Department of Computer Science, University of Sheffield, Sheffield, UK

    Ye Jiang

  7. CAS Key Laboratory of Geospace Environment, Department of Geophysics and Planetary Sciences, University of Science and Technology of China, Hefei, China

    Yuming Wang

  8. Department of Astronomy, Eötvös Loránd University, Budapest, Hungary

    Robert Erdélyi

  9. Gyula Bay Zoltán Solar Observatory (GSO), Hungarian Solar Physics Foundation (HSPF), Gyula, Hungary

    Robert Erdélyi

Authors
  1. Jiajia Liu
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  6. Yuming Wang
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  7. Robert Erdélyi
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Contributions

J.L. led and conducted the data preparation and data analysis and drafted the manuscript. Yimin Wang led and performed the machine learning approach with Y.J. and M.B.K. contributing to the discussions. R.E., X.H. and J.L. recognized the core problems. R.E. suggested and led the overall research. Yuming Wang helped with the automated detection of active regions. All authors contributed to discussions and participated in the interpretation of the results. All authors reviewed the manuscript.

Corresponding author

Correspondence to Robert Erdélyi.

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Competing interests

The authors declare no competing interests.

Additional information

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

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Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–4 and References 1–13.

Source data

Source Data Fig. 1

Source data for Fig. 1. Variables can be restored using IDL; use keyword /verb to see description of variables when restoring.

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Cite this article

Liu, J., Wang, Y., Huang, X. et al. Reliability of AI-generated magnetograms from only EUV images. Nat Astron 5, 108–110 (2021). https://doi.org/10.1038/s41550-021-01310-6

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