The Quasi-Radial Field-line Tracing (QRaFT): an Adaptive Segmentation of the Open-Flux Solar Corona

TL;DR

The paper introduces QRaFT, a new adaptive segmentation method for analyzing solar corona images to trace open magnetic field lines, improving understanding of the corona's magnetic structure and aiding space weather prediction.

Contribution

QRaFT is a novel image segmentation technique that accurately detects open magnetic field-aligned features in coronal images, validated with synthetic and real data.

Findings

QRaFT features align within 4-7 degrees of the magnetic field in models.

The method successfully analyzes real coronal images from space and ground-based observatories.

QRaFT enhances the empirical understanding of the corona's steady-state morphology.

Abstract

Optical observations of solar corona provide key information on its magnetic geometry. The large-scale open field of the corona plays an important role in shaping the ambient solar wind and constraining the propagation dynamics of the embedded structures, such as interplanetary coronal mass ejections. Rigorous analysis of the open-flux coronal regions based on coronagraph images can be quite challenging because of the depleted plasma density resulting in low signal-to-noise ratios. In this paper, we present an in-depth description of a new image segmentation methodology, the Quasi-Radial Field-line Tracing (QRaFT), enabling a detection of field-aligned optical coronal features approximating the orientation of the steady-state open magnetic field. The methodology is tested using synthetic coronagraph images generated by a three-dimensional magnetohydrodynamic model. The results of the numerical tests indicate that the extracted optical features are aligned within $\sim 4-7$ degrees with the local magnetic field in the underlying numerical solution. We also demonstrate the performance of the method on real-life coronal images obtained from a space-borne coronagraph and a ground-based camera. We argue that QRaFT outputs contain valuable empirical information about the global steady-state morphology of the corona which could help improving the accuracy of coronal and solar wind models and space weather forecasts.