TY - JOUR A1 - Verma, Meetu A1 - Matijevič, Gal A1 - Denker, Carsten A1 - Diercke, Andrea A1 - Dineva, Ekaterina Ivanova A1 - Balthasar, Horst A1 - Kamlah, Robert A1 - Kontogiannis, Ioannis A1 - Kuckein, Christoph A1 - Pal, Partha S. T1 - Classification of high-resolution Solar H alpha spectra using t-distributed stochastic neighbor embedding JF - The astrophysical journal : an international review of spectroscopy and astronomical physics N2 - The H alpha spectral line is a well-studied absorption line revealing properties of the highly structured and dynamic solar chromosphere. Typical features with distinct spectral signatures in H alpha include filaments and prominences, bright active-region plages, superpenumbrae around sunspots, surges, flares, Ellerman bombs, filigree, and mottles and rosettes, among others. This study is based on high-spectral resolution H alpha spectra obtained with the Echelle spectrograph of the Vacuum Tower Telescope (VTT) located at Observatorio del Teide, Tenerife, Spain. The t-distributed stochastic neighbor embedding (t-SNE) is a machine-learning algorithm, which is used for nonlinear dimensionality reduction. In this application, it projects H alpha spectra onto a two-dimensional map, where it becomes possible to classify the spectra according to results of cloud model (CM) inversions. The CM parameters optical depth, Doppler width, line-of-sight velocity, and source function describe properties of the cloud material. Initial results of t-SNE indicate its strong discriminatory power to separate quiet-Sun and plage profiles from those that are suitable for CM inversions. In addition, a detailed study of various t-SNE parameters is conducted, the impact of seeing conditions on the classification is assessed, results for various types of input data are compared, and the identified clusters are linked to chromospheric features. Although t-SNE proves to be efficient in clustering high-dimensional data, human inference is required at each step to interpret the results. This exploratory study provides a framework and ideas on how to tailor a classification scheme toward specific spectral data and science questions. KW - Solar chromosphere KW - Spectroscopy KW - Radiative transfer KW - Astronomy data KW - analysis KW - Astronomy databases KW - Astrostatistics tools Y1 - 2021 U6 - https://doi.org/10.3847/1538-4357/abcd95 SN - 1538-4357 VL - 907 IS - 1 PB - Institute of Physics Publ. CY - London ER - TY - JOUR A1 - Kamlah, Robert A1 - Verma, Meetu A1 - Diercke, Andrea A1 - Denker, Carsten T1 - Wavelength dependence of image quality metrics and seeing parameters and their relation to adaptive optics performance JF - Solar physics : a journal for solar and solar-stellar research and the study of solar terrestrial physics N2 - Ground-based solar observations are severely affected by Earth's turbulent atmosphere. As a consequence, observed image quality and prevailing seeing conditions are closely related. Partial correction of image degradation is nowadays provided in real time by adaptive optics (AO) systems. In this study, different metrics of image quality are compared with parameters characterizing the prevailing seeing conditions, i.e. Median Filter Gradient Similarity (MFGS), Median Filter Laplacian Similarity (MFLS), Helmli-Scherer mean, granular rms-contrast, differential image motion, and Fried-parameter r(0). The quiet-Sun observations at disk center were carried out at the Vacuum Tower Telescope (VTT), Observatorio del Teide (OT), Izana, Tenerife, Spain. In July and August 2016, time series of short-exposure images were recorded with the High-resolution Fast Imager (HiFI) at various wavelengths in the visible and near-infrared parts of the spectrum. Correlation analysis yields the wavelength dependence of the image quality metrics and seeing parameters, and Uniform Manifold Approximation and Projection (UMAP) is employed to characterize the seeing on a particular observing day. In addition, the image quality metrics and seeing parameters are used to determine the field dependence of the correction provided by the AO system. Management of high-resolution imaging data from large-aperture, ground-based telescopes demands reliable image quality metrics and meaningful characterization of prevailing seeing conditions and AO performance. The present study offers guidance on how retrieving such information ex post facto. KW - Granulation KW - Photosphere KW - Chromosphere KW - Image restoration KW - Adaptive KW - optics KW - Instrumentation and data management Y1 - 2021 U6 - https://doi.org/10.1007/s11207-021-01771-y SN - 1573-093X VL - 296 IS - 2 PB - Springer Science + Business Media B.V CY - Dordrecht [u.a.] ER -