Validation of Image Based Method for Assessing Coronal Magnetic Field Models

Coronal magnetic field models are key for accurate space weather forecasting. Observations of the solar corona provide key insight to the determination of the orientation of the Sun's magnetic field due to the frozen-in flux condition of plasma in the solar corona. The orientation of density features in the plasma of the solar corona can be treated as a proxy to the orientation of features in the coronal magnetic field due to this condition. Previous studies provided a method of using quasi-radial features detected in coronagraph images to improve coronal magnetic field models by comparing the orientation of the detected features to the projected orientation of the model fields (Jones et al., 2017, 2020). The disagreement is then quantified and used to optimize the coronal magnetic field model. As a part of these studies, coronal features are traced using an automated Quasi-Radial Feature Tracing (QRaFT) algorithm (Uritsky et al., 2022, in prep.) that uses adaptive thresholds to extract coronal features and approximate their orientation using polynomials. The orientation angles of these traced features are then used as input for optimizing and constraining the coronal magnetic field model. We work to validate this method using numerical outputs of an advanced solar coronal model developed by Predictive Science Inc. (PSI) for forecasting recent solar eclipses (Mikić et al., 2018). We compare features traced by the QRaFT algorithm in white-light coronagraph observations obtained by the K-Cor instrument at the ground-based Mauna-Loa Solar Observatory (MLSO K-COR) to features traced in synthetic polarization brightness (pB) images computed by PSI's Magnetohydrodynamic Algorithm outside a Sphere (MAS) code, where the solution is known (Mikić et al., 2018, and references therein). We use the magnetic field parameters generated by this model to correlate how well each feature matches the expected magnetic orientation for both the synthetic pB images and MLSO K-COR observations. Preliminary results show close correlation between the performance of the QRaFT algorithm on the synthetic pB images and MLSO K-COR images when compared to the expected magnetic orientation from the model. We show proof of concept of this approach, as well as plans for future work and improvements to the method.