Fast and Accurate Emulation of the SDO/HMI Pipeline with Uncertainty Quantification
Channel: COSPAR2021 Workshops
Date: 2021-02-02

Authors: Richard E.L. Higgins, David Fouhey, Dichang Zhang, Spiro K Antiochos, Graham Barnes, Jon Hoeksema, K.D. Leka, Yang Liu, Peter Schuck, Tamas Gombosi

Abstract: Space weather has impacts on Earth ranging from rare, immensely disruptive events like electrical blackouts caused by solar flares to more daily impacts like satellite drag and GPS interference caused by increased extreme UV absorption in the Earth’s ionosphere. Accordingly, there are multiple instruments observing the photospheric magnetic field at a variety of duty cycles, spatial resolutions, and temporal cadences. One such instrument is the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory (SDO/HMI), which produces full-disk vector magnetic field maps with a cadence and resolution optimized to study the photospheric plasma's relation to these space weather phenomena. The magnetogram products produced by HMI and its analysis pipeline are the result of a per-pixel optimization that estimates atmospheric parameters that minimize disagreement between a synthesized and observed Stokes vector. In this paper, we introduce a deep learning-based approach that can emulate the existing SDO/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 solve this with a regression-by-classification approach. 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 per-pixel. We additionally show that despite being trained per-pixel, our system is able to faithfully reproduce known systematic oscillations in full-disk statistics produced by the pipeline. This system could serve as both an initialization for the full system and as an ultra-fast proxy for the system in space weather applications. This work is part of the SOLSTICE center and will be open sourced.