A Machine-learning Data Set Prepared from the NASA Solar Dynamics Observatory Mission

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

doi:10.3847/1538-4365/ab1005

Abstract

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.

Highlights

Launched in 2010, NASA’s Solar Dynamics Observatory (SDO; Pesnell et al 2012) has been continuously monitoring the Sun’s activity and delivering valuable scientific data for heliophysics researchers with the use of three instruments: 1. The Atmospheric Imaging Assembly (AIA; Lemen et al 2012), which captures 4096 × 4096 resolution images of the full Sun in two ultraviolet (UV; centered at 1600 and 1700 Å) wavelength bands, seven extreme ultraviolet (EUV) wavelength bands and one visible wavelength.2
Calibrated level 1 scientific data from the AIA and Helioseismic and Magnetic Imager (HMI) instruments are accessible from the Joint Science Operations Center11 (JSOC) at Stanford University, Lockheed Martin Solar & Astrophysics Laboratory, and affiliate science data centers, while science data from the EUV Variability Experiment (EVE) instrument are accessible from the EVE Science Operations Center12 at the Laboratory for Atmospheric and Space Physics at the University of Colorado, Boulder
Our aim is to supply this standardized data set for heliophysicists who wish to use machine learning in their own research, as well as machine-learning researchers who wish to develop models specialized for the physical sciences

Summary

Introduction

The Atmospheric Imaging Assembly (AIA; Lemen et al 2012), which captures 4096 × 4096 resolution images (with 0.6 arcsec pixel size) of the full Sun in two ultraviolet (UV; centered at 1600 and 1700 Å) wavelength bands, seven extreme ultraviolet (EUV) wavelength bands (centered at 94, 131, 171, 193, 211, 304, and 335 Å) and one visible wavelength (centered at 4500 Å). In the eight years after launch, over 3000 refereed scientific publications have made use of SDO data. This success can be attributed to the reliability of the spacecraft and its instruments, the consistency and quality of the observations, the mission’s open data policy, and the ease of online data access from the affiliated science data centers. Deep-learning applications have began to emerge from the heliophysics

Objectives

Results

Conclusion