In this paper we present a curated dataset 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, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and translating HMI observations into AIA observations. For each application we provide metrics and baselines for future model comparison. We anticipate this curated dataset 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 dataset.
@article{arxiv.1903.04538,
title = {A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission},
author = {Richard Galvez and David F. Fouhey and Meng Jin and Alexandre Szenicer and Andrés Muñoz-Jaramillo and Mark C. M. Cheung and Paul J. Wright and Monica G. Bobra and Yang Liu and James Mason and Rajat Thomas},
journal= {arXiv preprint arXiv:1903.04538},
year = {2019}
}
Comments
Accepted to The Astrophysical Journal Supplement Series; 11 pages, 8 figures