  The Helioseismic and Magnetic Imager (HMI) onboard NASA's Solar Dynamics
Observatory (SDO) produces estimates of the photospheric magnetic field which
are a critical input to many space weather modelling and forecasting systems.
The magnetogram products produced by HMI and its analysis pipeline are the
result of a per-pixel optimization that estimates solar atmospheric parameters
and minimizes disagreement between a synthesized and observed Stokes vector. In
this paper, we introduce a deep learning-based approach that can emulate the
existing HMI pipeline results two orders of magnitude faster than the current
pipeline algorithms. Our system is a U-Net trained on input Stokes vectors and
their accompanying optimization-based VFISV inversions. We demonstrate that our
system, once trained, can produce high-fidelity estimates of the magnetic field
and kinematic and thermodynamic parameters while also producing meaningful
confidence intervals. We additionally show that despite penalizing only
per-pixel loss terms, our system is able to faithfully reproduce known
systematic oscillations in full-disk statistics produced by the pipeline. This
emulation system could serve as an initialization for the full Stokes inversion
or as an ultra-fast proxy inversion. This work is part of the NASA Heliophysics
DRIVE Science Center (SOLSTICE) at the University of Michigan, under grant NASA
80NSSC20K0600E, and has been open sourced.

Solar and Stellar Astrophysics;Instrumentation and Methods for Astrophysics;Computer Vision and Pattern Recognition