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      High-Cadence Imaging and Imaging Spectroscopy at the GREGOR Solar Telescope - A Collaborative Research Environment for High-Resolution Solar Physics


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          In high-resolution solar physics, the volume and complexity of photometric, spectroscopic, and polarimetric ground-based data significantly increased in the last decade reaching data acquisition rates of terabytes per hour. This is driven by the desire to capture fast processes on the Sun and by the necessity for short exposure times "freezing" the atmospheric seeing, thus enabling post-facto image restoration. Consequently, large-format and high-cadence detectors are nowadays used in solar observations to facilitate image restoration. Based on our experience during the "early science" phase with the 1.5-meter GREGOR solar telescope (2014-2015) and the subsequent transition to routine observations in 2016, we describe data collection and data management tailored towards image restoration and imaging spectroscopy. We outline our approaches regarding data processing, analysis, and archiving for two of GREGOR's post-focus instruments (see, i.e., the GREGOR Fabry-Perot Interferometer (GFPI) and the newly installed High-Resolution Fast Imager (HiFI). The heterogeneous and complex nature of multi-dimensional data arising from high-resolution solar observations provides an intriguing but also a challenging example for "big data" in astronomy. The big data challenge has two aspects: (1) establishing a workflow for publishing the data for the whole community and beyond and (2) creating a Collaborative Research Environment (CRE), where computationally intense data and post-processing tools are co-located and collaborative work is enabled for scientists of multiple institutes. This requires either collaboration with a data center or frameworks and databases capable of dealing with huge data sets based on Virtual Observatory (VO) and other community standards and procedures.

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          Image Quality in High-resolution and High-cadence Solar Imaging

          Broad-band imaging and even imaging with a moderate bandpass (about 1 nm) provides a "photon-rich" environment, where frame selection ("lucky imaging") becomes a helpful tool in image restoration allowing us to perform a cost-benefit analysis on how to design observing sequences for high-spatial resolution imaging in combination with real-time correction provided by an adaptive optics (AO) system. This study presents high-cadence (160 Hz) G-band and blue continuum image sequences obtained with the High-resolution Fast Imager (HiFI) at the 1.5-meter GREGOR solar telescope, where the speckle masking technique is used to restore images with nearly diffraction-limited resolution. HiFI employs two synchronized large-format and high-cadence sCMOS detectors. The Median Filter Gradient Similarity (MFGS) image quality metric is applied, among others, to AO-corrected image sequences of a pore and a small sunspot observed on 2017 June 4 and 5. A small region-of-interest, which was selected for fast imaging performance, covered these contrast-rich features and their neighborhood, which were part of active region NOAA 12661. Modifications of the MFGS algorithm uncover the field- and structure-dependency of this image quality metric. However, MFGS still remains a good choice for determining image quality without a priori knowledge, which is an important characteristic when classifying the huge number of high-resolution images contained in data archives. In addition, this investigation demonstrates that a fast cadence and millisecond exposure times are still insufficient to reach the coherence time of daytime seeing. Nonetheless, the analysis shows that data acquisition rates exceeding 50 Hz are required to capture a substantial fraction of the best seeing moments, significantly boosting the performance of post-facto image restoration.

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            27 February 2018


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            15 pages, 5 figures, accepted for publication in The Astrophysical Journal Supplement Series (Special Issue on Management, Search and Analysis of Solar and Stellar Astronomy Big Data (SABID))
            astro-ph.IM astro-ph.SR


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