Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation

TL;DR

This paper introduces CHIMERA, an algorithm that uses multi-thermal EUV images to accurately identify and segment coronal holes, improving space weather forecasting by linking coronal hole properties to geomagnetic storm duration.

Contribution

The novel CHIMERA algorithm utilizes multi-passband EUV data for precise coronal hole segmentation, surpassing previous single-passband methods and enabling better space weather predictions.

Findings

Accurate segmentation of coronal holes using multi-thermal images.

Identified linear relationship between coronal hole area and geomagnetic storm duration.

CHIMERA enhances forecasting of geomagnetic storm onset and duration.

Abstract

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single EUV passband and magnetogram images to extract CH information. Here, the Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) is described, which analyses multi-thermal images from the Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms.