A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
This work facilitates machine learning research in heliophysics and physical sciences by providing a curated dataset, though it is incremental as it focuses on data preparation rather than novel methods.
The paper tackles the challenge of making NASA Solar Dynamics Observatory data accessible for machine learning by curating and processing it into a manageable format, and demonstrates its utility with example applications like forecasting irradiance and translating observations, providing metrics for future comparison.
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.