Incorporating Magnetic Field Characteristics into EUV-Based Automated Segmentation of Coronal Holes

TL;DR

This paper enhances EUV-based coronal hole segmentation by integrating magnetic field data, improving accuracy in identifying regions relevant to space weather prediction.

Contribution

It introduces a novel method that incorporates magnetic field information directly into the segmentation process, reducing false positives and capturing difficult-to-identify CH regions.

Findings

Improved accuracy in coronal hole detection.

Reduced false positives from filaments.

Enhanced segmentation robustness against intensity variations.

Abstract

Coronal holes (CHs) are magnetically open regions that allow hot coronal plasma to escape from the Sun and form the high-speed solar wind. This wind can interact with Earth's magnetic field. For this reason, developing an accurate understanding of CH regions is vital for understanding space weather and its effects on Earth. The process of identifying CH regions typically relies on extreme ultraviolet (EUV) imagery, leveraging the fact that CHs appear dark at these wavelengths. Accurate identification of CHs in EUV, however, can be difficult due to a variety of factors, including stray light from nearby regions, limb brightening, and the presence of filaments (which also appear dark, but are not sources of solar wind). In order to overcome these issues, this work incorporates photospheric magnetic field data into a classical EUV-based segmentation algorithm based on the active contours without edges (ACWE) segmentation method. In this work magnetic field data are incorporated directly into the segmentation process, serving both as a method for removing non-CH regions in advance, and as a method to constrain evolution of the segmented CH boundary. This reduces the presence of filaments while allowing the segmentation to include CH regions that may be difficult to identify due to inconsistent intensities.