Spectroflat: A generic spectrum and flat-field calibration library for spectro-polarimetric data

• Published in: Astronomy and astrophysics (Berlin) Vol. 687; p. A22
• Main Authors: Hölken, J., Doerr, H.-P., Feller, A., Iglesias, F. A.
• Format: Journal Article
• Language: English
• Published: Heidelberg EDP Sciences 01.07.2024
• Subjects: Algorithms; Calibration; Data acquisition; Distortion; Dust; High resolution; Image resolution; Line spectra; Near ultraviolet radiation; Noise intensity; Noise levels; Orthogonality; Polarimetry; Python; Sensors; Spatial data; Spectrographs; Spectrometers
• ISSN: 0004-6361; 1432-0746
• DOI: 10.1051/0004-6361/202348877

Context . Flat-fielding spectro-polarimetric data with one spatial and one spectral dimension is inherently difficult as the imprint of the spectral lines needs to be separated from other wavelength-dependent instrumental effect (e.g., fringes or prefilter profiles) and wavelength-independent effects (e.g., dust and sensor response). Current approaches for spectrometers are often based on moving the grating or they depend on optical models and/or on lab calibration data. They are also limited to small spectral regions and are instrument-specific. Approaches that would be suitable for polarimeters have not been reported yet. Aims . We present an approach that allows for flat-field calibration data to be to obtained for diffraction-grating-based, long-slit spec-trographs combined with temporally modulated polarimetry from high-resolution solar telescopes. This approach is based on nominal flat-fielding procedures performed during the instrument’s science operations. Methods . We performed a precise and field-dependent correction of the spectrographic distortion effect (resulting in curved spectral lines, typically denoted as a “smile” effect) to ensure the orthogonality of the spectral and spatial dimensions. We identified distortions by tracking the position of multiple spectral lines within the full spectral field of view. From the raw modulated flats, we then removed the solar line imprints and derived separate flat-fields for sensor and slit dust features. Optionally, wavelength calibration and continuum correction can be included in this process. Results . We have created generic Python libraries that can be plugged into existing Python-based data reduction pipelines or used as a standalone calibration tool. We show that for spectrographs covering many spectral lines, a correction of the smile distortion based on optical models alone is not sufficient. Our results demonstrate a suppression of fringes, sensor artifacts, and fixed-pattern imprints in demodulated data by one order of magnitude. For intensity images, the photon noise level can be closely attained after calibration. Our correction works across the full spectral range. The algorithm was tested for different wavelength regimes with emission (EUV range) or absorption (near-UV, VIS, IR range) spectra, on data acquired with ground-based (SST/TRIPPLE-SP, GREGOR/GRIS), balloon-borne (SUNRISE-III/SUSI), and space-based (SolO/SPICE) instruments. The data calibrated with our method offer robust and precise inversion results. Conclusions . We have extended existing spectroscopic flat-field techniques to modern instruments with large imaging sensors covering many spectral lines simultaneously, and with polarimetric capabilities, where methods described so far are not adequate. We believe that our method is applicable as a standard calibration approach for most modern high resolution large-FOV, long-slit spectrographs – both with and without polarimetric capabilities.