Over the past 50 years, a variety of instruments have obtained images of the Sun’s magnetic field (magnetograms) to study its origin and evolution. While improvements in instrumentation have led to breakthroughs in our understanding of physical phenomena, differences between subsequent instruments such as resolution, noise, and saturation levels all introduce inhomogeneities into long-term data sets. This has proven to be an insurmountable obstacle for research applications that require high-resolution and homogeneous data spanning time frames longer than the lifetime of a single instrument.
Here we show that deep-learning-based super-resolution techniques can successfully up-sample and homogenize solar magnetic field images obtained both by space and ground-based instruments. In particular, we show the results of cross-calibrating and super-resolving MDI and GONG magnetograms to the characteristics of HMI.
We also discuss the importance of agreeing on a standardized set of training, validation, and test data, as well as metrics that enable the community to benchmark different approaches to collectively and quantitatively identify the best practices. This includes distributing test data within the broad heliophysics community.
Finally, we discuss our approach for making an empirical estimation of uncertainty and the importance that uncertainty estimation plays in the credibility and usefulness of deep learning applications in heliophysics.