SynthIA: A Synthetic Inversion Approximation for the Stokes Vector Fusing SDO and Hinode into a Virtual Observatory

Both NASA's Solar Dynamics Observatory (SDO) and the JAXA/NASA Hinode mission include spectropolarimetric instruments designed to measure the photospheric magnetic field. SDO's Helioseismic and Magnetic Imager (HMI) emphasizes full-disk, high-cadence, and good-spatial-resolution data acquisition while Hinode's Solar Optical Telescope Spectro-Polarimeter (SOT-SP) focuses on high spatial resolution and spectral sampling at the cost of a limited field of view and slower temporal cadence. This work introduces a deep-learning system, named the Synthetic Inversion Approximation (SynthIA), that can enhance both missions by capturing the best of each instrument's characteristics. We use SynthIA to produce a new magnetogram data product, the Synthetic Hinode Pipeline (SynodeP), that mimics magnetograms from the higher-spectral-resolution Hinode/SOT-SP pipeline, but is derived from full-disk, high-cadence, and lower-spectral-resolution SDO/HMI Stokes observations. Results on held-out data show that SynodeP has good agreement with the Hinode/SOT-SP pipeline inversions, including magnetic fill fraction, which is not provided by the current SDO/HMI pipeline. SynodeP further shows a reduction in the magnitude of the 24 hr oscillations present in the SDO/HMI data. To demonstrate SynthIA's generality, we show the use of SDO/Atmospheric Imaging Assembly data and subsets of the HMI data as inputs, which enables trade-offs between fidelity to the Hinode/SOT-SP inversions, number of observations used, and temporal artifacts. We discuss possible generalizations of SynthIA and its implications for space-weather modeling. This work is part of the NASA Heliophysics DRIVE Science Center at the University of Michigan under grant NASA 80NSSC20K0600E, and will be open-sourced.