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

Abstract

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.

Accepted to The Astrophysical Journal Supplement Series; 11 pages, 8 figures

References in corpus (4)

• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Photometric and Thermal Cross-calibration of Solar EUV Instruments
• Flare Prediction Using Photospheric and Coronal Image Data

Cited by in corpus (26)

• Solar wind prediction using deep learning
• CNN-Based Deep Learning in Solar Wind Forecasting
• Analysis of full disc Ca II K spectroheliograms III. Plage area composite series covering 1892-2019
• SMARPs and SHARPs: Two Solar Cycles of Active Region Data
• Multi-channel coronal hole detection with convolutional neural networks
• A New Automatic Tool for CME Detection and Tracking with Machine Learning Techniques
• SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory
• Machine-learning approach to identification of coronal holes in solar disk images and synoptic maps
• Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning
• SCSS-Net: Solar Corona Structures Segmentation by Deep Learning
• Inferring Maps of the Sun's Far-side Unsigned Magnetic Flux from Far-side Helioseismic Images using Machine Learning Techniques
• Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation
• Properties of Flare-Imminent versus Flare-Quiet Active Regions from the Chromosphere through the Corona I: Introduction of the AIA Active Region Patches (AARPs)
• Image Quality Assessment for Full-Disk Solar Observations with Generative Adversarial Networks
• Attention-Based Generative Neural Image Compression on Solar Dynamics Observatory
• Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning
• Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
• Neural-based Compression Scheme for Solar Image Data
• Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona
• Using Multiple Instance Learning for Explainable Solar Flare Prediction
• Proxy-Based Prediction of Solar Extreme Ultraviolet Emission using Deep Learning
• Evaluating Four FPGA-accelerated Space Use Cases based on Neural Network Algorithms for On-board Inference
• Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning
• ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
• Simultaneously forecasting global geomagnetic activity using Recurrent Networks
• Spectral and Spatial Graph Learning for Multispectral Solar Image Compression