A machine-learning data set prepared from the NASA solar dynamics observatory mission

Richard Galvez, David F. Fouhey, Meng Jin, Alexandre Szenicer, Andrés Muñoz-Jaramillo, Mark C.M. Cheung, Paul J. Wright, Monica G. Bobra, Yang Liu, James Mason, Rajat Thomas

Research output: Contribution to journalArticlepeer-review


In this paper, we present a curated data set from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine-learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, down-sampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this data set with two example applications: forecasting future extreme ultraviolet (EUV) Variability Experiment (EVE) irradiance from present EVE irradiance and translating Helioseismic and Magnetic Imager observations into Atmospheric Imaging Assembly observations. For each application, we provide metrics and baselines for future model comparison. We anticipate this curated data set will facilitate machine-learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the Appendix for access to the data set, totaling 6.5TBs.

Original languageEnglish (US)
Article numberY
JournalAstrophysical Journal, Supplement Series
Issue number1
StatePublished - 2019


  • Astronomical databases: miscellaneous
  • Catalogs
  • Editorials, notices
  • Miscellaneous
  • Surveys

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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