SSTRED: Data- and metadata-processing pipeline for CHROMIS and CRISP
arXiv:1804.03030 · doi:10.1051/0004-6361/202141326
Abstract
Context: Data from ground-based, high-resolution solar telescopes can only be used for science with calibrations and processing, which requires detailed knowledge about the instrumentation. [...] Aims: We aim to provide observers with a user-friendly data pipeline for data from the Swedish 1-meter Solar Telescope (SST) that delivers science-ready data together with the metadata needed for proper interpretation and archiving. Methods: We briefly describe the [instrumentation]. We summarize the processing steps from raw data to science-ready data cubes in FITS files. We report calibrations and compensations for data imperfections in detail. Misalignment of \ion{Ca}{ii} data due to wavelength-dependent dispersion is identified, characterized, and compensated for. We describe intensity calibrations that remove or reduce the effects of filter transmission profiles as well as solar elevation changes. We present REDUX, a new version of the MOMFBD image restoration code, with multiple enhancements and new features. [...] We describe how image restoration is used [...]. The science-ready output is delivered in FITS files, with metadata compliant with the SOLARNET recommendations. Data cube coordinates are specified within the World Coordinate System (WCS). Cavity errors are specified as distortions of the WCS wavelength coordinate with an extension of existing WCS notation. We establish notation for specifying the reference system for Stokes vectors [...]. [CRISPEX] has been extended to accept SSTRED output [...]. Results: SSTRED is a mature data-processing pipeline for imaging instruments, developed and used for the SST/CHROMIS imaging spectrometer and the SST/CRISP spectropolarimeter. SSTRED delivers well-characterized, science-ready, archival-quality FITS files with well-defined metadata. The SSTRED code, as well as REDUX and CRISPEX, is freely available through git repositories.
Accepted for publication in A&A
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- The making of robust and highly performing imaging spectropolarimeters for large solar telescopes
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- Automatic detection of Ellerman bombs using Deep Learning
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