An Improved Data-Driven Analysis Pipeline for Coronal Hole Detection and Mapping

Abstract:

The detection and mapping of Coronal Holes (CH) in solar Extreme UltraViolet (EUV) and X-Ray images is useful for a variety of scientific and space weather prediction applications. In this presentation, we discuss a community-oriented python package being developed to facilitate the creation of science quality, full-sun, coronal hole maps. Our package builds upon a previous pipeline for multi-spacecraft synchronic mapping and detection (Caplan et al. 2016), where long-term averages of EUVI/STEREO A/B (195 Å) and SDO/AIA (193 Å) images are used to compute data-derived corrections for center-to-limb variations in images and intensity differences among instruments. The calculation of these corrections has been greatly simplified through modern database storage and querying techniques implemented in the image processing pipeline. After image processing, CH regions are detected using our two-threshold region-growing algorithm, which uses pixel-connectivity requirements to avoid false detections. Results are mapped on an image-by-image basis and merged to create EUV and CH maps. The merge-mapping process favors lower intensity data in areas of overlap, and an arbitrary number of images may be used, helping to mitigate the systematic issues of obscuration and evolution. With this flexible new framework and data-driven approach to CH detection, we explore methods for creating time-dependent coronal hole maps and evaluate their use for model comparisons.