Identification of Coronal Holes on AIA/SDO images using unsupervised Machine Learning

TL;DR

This study employs an unsupervised k-means clustering approach on AIA/SDO solar images to identify coronal holes, offering a simpler alternative to complex methods and emphasizing the need for a standardized coronal hole database.

Contribution

The paper introduces an unsupervised k-means method for coronal hole detection on solar images, demonstrating its effectiveness and advocating for a standardized CH boundary database.

Findings

k-means clustering produces results comparable to CNN-based methods

Systematic pre- and post-processing improve clustering accuracy

Highlights the necessity of a consensus CH boundary database

Abstract

Through its magnetic activity, the Sun governs the conditions in Earth's vicinity, creating space weather events, which have drastic effects on our space- and ground-based technology. One of the most important solar magnetic features creating the space weather is the solar wind, that originates from the coronal holes (CHs). The identification of the CHs on the Sun as one of the source regions of the solar wind is therefore crucial to achieve predictive capabilities. In this study, we used an unsupervised machine learning method, $k$-means, to pixel-wise cluster the passband images of the Sun taken by the Atmospheric Imaging Assembly on {\it the Solar Dynamics Observatory} (AIA/SDO) in 171 \AA, 193 \AA\,, and 211 \AA\,in different combinations. Our results show that the pixel-wise $k$-means clustering together with systematic pre- and post-processing steps provides compatible results with those from complex methods, such as CNNs. More importantly, our study shows that there is a need for a CH database that a consensus about the CH boundaries are reached by observers independently. This database then can be used as the "ground truth", when using a supervised method or just to evaluate the goodness of the models.