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This paper presents an intramethod ensemble for coronal hole (CH) detectionbased on the Active Contours Without Edges (ACWE) segmentation algorithm. Thepurpose of this ensemble is to develop a confidence map that defines, for allon disk regions of a Solar extreme ultraviolet (EUV) image, the likelihood thateach region belongs to a CH based on that region's proximity to, andhomogeneity with, the core of identified CH regions. CHs are regions of openmagnetic field lines, resulting in high speed solar wind. Accurate detection ofCHs is vital for space weather prediction. By relying on region homogeneity,and not intensity (which can vary due to various factors including line ofsight changes and stray light from nearby bright regions), to define the finalconfidence of any given region, this ensemble is able to provide robust,consistent delineations of the CH regions. Using the metrics of globalconsistency error (GCE), local consistency error (LCE), intersection over union(IOU), and the structural similarity index measure (SSIM), the method is shownto be robust to different spatial resolutions and different intensityresolutions. Furthermore, using the same metrics, the method is shown to berobust across short timescales, indicating self-consistent segmentations.Finally, the accuracy of the segmentations and confidence maps are validated byconsidering the skewness (i.e., unipolarity) of the underlying magnetic field.

Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours without Edges (ACWE)