2015/10/31 by Ronald M. Caplan, R. M. Caplan, Cooper Downs +3 · 76 citations
Earth and Planetary Sciences · Physics and Astronomy · #Adaptive optics and wavefront sensing #Artificial intelligence #Computer science #Coronal plane #Extreme ultraviolet #Extreme ultraviolet lithography #Geophysics and Gravity Measurements #Laser #Normalization (sociology) #Optics #Physics #Preprocessor #Segmentation #Solar and Space Plasma Dynamics #astro-ph.SR
paper · pdf · doi:10.3847/0004-637x/823/1/53
published in The Astrophysical Journal 823(1), 53 (IOP Publishing) · 37 pages, 24 figures
openalex publication_date 2016/05/20 · arxiv created 2016/05/25 · arxiv updated 2016/05/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
ABSTRACT We describe a method for the automatic mapping of coronal holes (CHs) using simultaneous multi-instrument EUV imaging data. Synchronized EUV images from STEREO /EUVI A and B 195 Å and SDO /AIA 193 Å are preprocessed, including point-spread function deconvolution and the application of data-derived intensity corrections which account for center-to-limb variations (limb brightening) and inter-instrument intensity normalization. We systematically derive a robust limb-brightening correction that takes advantage of unbiased long-term averages of data and respects the physical nature of the problem. The new preprocessing greatly assists in CH detection, allowing for the use of a simplified variable-connectivity, two-threshold region-growing image segmentation algorithm to obtain consistent detection results. We generate synchronic EUV and CH maps, and show a preliminary analysis of CH evolution. Several data and code products are made available to the community ( www.predsci.com/chd ): for the period of this study (2010 June 10 to 2014 August 18), we provide synchronic EUV and CH map data at 6 hr cadence, data-derived limb-brightening corrections for STEREO /EUVI A and B 195 Å and SDO /AIA 193 Å, and inter-instrument correction factors to equate their intensities. We also provide the CH image segmentation code module ( ezseg ) implemented in both FORTRAN-OpenMP and GPU-accelerated C-CUDA. A complete implementation of our CH detection pipeline in the form of a ready-to-use MATLAB driver script euv2chm utilizing ezseg is also made available.