SuperSynthIA: Physics-ready Full-disk Vector Magnetograms from HMI, Hinode, and Machine Learning
Ruoyu Wang, David F. Fouhey, Richard E. L. Higgins, Spiro K. Antiochos, Graham Barnes, and 5 more authors
The Astrophysical Journal, Jul 2024
Vector magnetograms of the Sun’s photosphere are cornerstones for much of solar physics research. These data are often produced by data-analysis pipelines combining per-pixel Stokes polarization vector inversion with a disambiguation that resolves an intrinsic 180° ambiguity. We introduce a learning-based method, SuperSynthIA, that produces full-disk vector magnetograms from Stokes vector observations. As input, SuperSynthIA uses Stokes polarization images from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). As output, SuperSynthIA simultaneously emulates the inversion and disambiguation outputs from the Hinode/Solar Optical Telescope-Spectro-Polarimeter (SOT-SP) pipeline. Our method extends our previous approach SynthIA with heliographic outputs as well as using an improved data set and inference method. SuperSynthIA provides a new tool for improved magnetic fields from full-disk SDO/HMI observations using information derived from the enhanced capabilities of Hinode/SOT-SP. Compared to our previous SynthIA, SuperSynthIA provides physics-ready vector magnetograms and mitigates unphysical angle preferences and banding artifacts in SynthIA. SuperSynthIA data are substantially more temporally consistent than those from the SDO/HMI pipeline, most notably seen in, e.g., evolving active regions. SuperSynthIA substantially reduces noise in low-signal areas, resulting in less center-to-limb bias outside of strong-signal areas. We show that outputs from SuperSynthIA track the SDO/HMI-recorded evolution of the magnetic field. We discuss the limitations of SuperSynthIA that the user must understand, and we demonstrate a broad set of evaluations to test SuperSynthIA and discuss remaining known artifacts. Our tests provide both methodology and evidence that SuperSynthIA outputs are ready for use by the community, and that learning-based approaches are suitable for physics-ready magnetograms.
