A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission

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 resoluti...

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Bibliographic Details
Published in:arXiv.org 2019-03
Main Authors: Galvez, Richard, Fouhey, David F, Meng, Jin, Szenicer, Alexandre, Muñoz-Jaramillo, Andrés, Cheung, Mark C M, Wright, Paul J, Bobra, Monica G, Liu, Yang, Mason, James, Thomas, Rajat
Format: Article
Language:English
Subjects:
Artificial intelligence
Datasets
Helioseismology
Irradiance
Machine learning
Observatories
Physical sciences
Solar activity
Solar observatories
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1903.04538