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

Galvez, R. et al. (2019) A machine-learning data set prepared from the NASA solar dynamics observatory mission. Astrophysical Journal Supplement Series, 242(1), 7. (doi: 10.3847/1538-4365/ab1005)

Abstract

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wright, Mr Paul
• Authors: Galvez, R., Fouhey, D. F., Jin, M., Szenicer, A., Muñoz-Jaramillo, A., Cheung, M. C.M., Wright, P. J., Bobra, M. G., Liu, Y., Mason, J., and Thomas, R.
• College/School: College of Science and Engineering > School of Physics and Astronomy
• Journal Name: Astrophysical Journal Supplement Series
• Publisher: American Astronomical Society
• ISSN: 0067-0049
• ISSN (Online): 1538-4365
• Published Online: 08 May 2019
• Copyright Holders: Copyright © 2019 The American Astronomical Society
• First Published: First published in Astrophysical Journal Supplement Series 242(1): 7
• Publisher Policy: Reproduced under a Creative Commons License