Quantitative Image-Based Validation Framework for Assessing Global Coronal Magnetic Field Models

Coronagraph observations provide key information about the orientation of the Sun's magnetic field. Previous studies used various algorithms to segment quasi-radial features in coronagraph images and approximate their local plane-of-sky geometry and orientation which can be used as input for optimizing and constraining coronal magnetic field models. We present a new framework that allows for further quantitative evaluations of image-based coronal segmentation methods against magnetic field models, and vice-versa. We compare quasi-radial features identified from QRaFT, a global coronal feature tracing algorithm, in white-light coronagraph images to outputs of MAS, an advanced magnetohydrodynamic model. We use the FORWARD toolset to produce synthetic polarized brightness images co-aligned to real coronagraph observations, segment features in these images, and quantify the difference between the inferred and model magnetic field. This approach allows us to geometrically compare features segmented in artificial images to those segmented in white-light coronagraph observations against the plane-of-sky projected MAS coronal magnetic field. We quantify QRaFT's performance in the artificial images and observational data, and perform statistical analyses that measure the accuracy and uncertainty of the model output to the observational data. The results demonstrate that a coronal segmentation method identifies the global large-scale orientation of the coronal magnetic field within $\sim\pm10^\circ$ of the plane-of-sky projected MAS magnetic field.