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

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 Å, 193 Å\,, and 211 Å\,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.

References in corpus (13)

• Deep Learning in Neural Networks: An Overview
• scikit-image: Image processing in Python
• Coronal Holes
• Heliophysics Event Knowledgebase for the Solar Dynamics Observatory and Beyond
• Automated Coronal Hole Detection using Local Intensity Thresholding Techniques
• Statistical Analysis and Catalog of Non-polar Coronal Holes Covering the SDO-era using CATCH
• Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation
• Segmentation of coronal holes in solar disk images with a convolutional neural network
• Multi-channel coronal hole detection with convolutional neural networks
• A Model for Coronal Hole Bright Points and Jets due to Moving Magnetic Elements
• The Observational Uncertainty of Coronal Hole Boundaries in Automated Detection Schemes
• 3-Phase Evolution of a Coronal Hole, Part II: The Magnetic Field
• How to Estimate the Far-Side Open Flux using STEREO Coronal Holes