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Found 4 papers, 4 papers with code
Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation
The Solar Dynamics Observatory (SDO), a NASA multi-spectral decade-long mission that has been daily producing terabytes of observational data from the Sun, has been recently used as a use-case to demonstrate the potential of machine learning methodologies and to pave the way for future deep-space mission planning.
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning
Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.
Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona
The synergy between machine learning and this enormous amount of data has the potential, still largely unexploited, to advance our understanding of the Sun and extend the capabilities of heliophysics missions.
Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning
As a part of NASA's Heliophysics System Observatory (HSO) fleet of satellites, the Solar Dynamics Observatory (SDO) has continuously monitored the Sun since2010.
