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GST Data-processing Workflow: Image Registration and Alignment
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AI
DOI：10.3847/1538-4365/ac91c9.png)
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Multiple solar instrument observation campaigns are increasingly popular among the solar physics and space science communities. Scientists organize high-resolution ground-based telescopes and spacecraft to study the evolution of the complex solar atmosphere and the origin of space weather. Image registration and coalignment between different instruments are vital for accurate data product comparison. We developed a Python language package for registration of ground-based high-resolution imaging data acquired by the Goode Solar Telescope (GST) to space-based full-disk continuum intensity data provided by the Solar Dynamics Observatory (SDO) with the scale-invariant feature transform method. The package also includes tools to align data sets obtained in different wavelengths and at different times utilizing the optical flow method. We present the image registration and coalignment workflow. The aliment accuracy of each alignment method is tested with the aid of radiative magnetohydrodynamics simulation data. We update the pointing information in GST data fits headers and generate GST and SDO imaging data products as science-ready four-dimensional (x, y, lambda, t) data cubes.
Keyword:
SOLAR-TELESCOPE
期刊
IF:
8.5
论文数:
5.7K
被引数:
4.2W
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