@article{VermaMatijevičDenkeretal.2021,
  author    = {Verma, Meetu and Matijevič, Gal and Denker, Carsten and Diercke, Andrea and Dineva, Ekaterina Ivanova and Balthasar, Horst and Kamlah, Robert and Kontogiannis, Ioannis and Kuckein, Christoph and Pal, Partha S.},
  title     = {Classification of high-resolution Solar H alpha spectra using t-distributed stochastic neighbor embedding},
  series = {The astrophysical journal : an international review of spectroscopy and astronomical physics},
  volume    = {907},
  journal   = {The astrophysical journal : an international review of spectroscopy and astronomical physics},
  number    = {1},
  publisher = {Institute of Physics Publ.},
  address   = {London},
  issn      = {1538-4357},
  doi       = {10.3847/1538-4357/abcd95},
  pages     = {14},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{KamlahVermaDierckeetal.2021,
  author    = {Kamlah, Robert and Verma, Meetu and Diercke, Andrea and Denker, Carsten},
  title     = {Wavelength dependence of image quality metrics and seeing parameters and their relation to adaptive optics performance},
  series = {Solar physics : a journal for solar and solar-stellar research and the study of solar terrestrial physics},
  volume    = {296},
  journal   = {Solar physics : a journal for solar and solar-stellar research and the study of solar terrestrial physics},
  number    = {2},
  publisher = {Springer Science + Business Media B.V},
  address   = {Dordrecht [u.a.]},
  issn      = {1573-093X},
  doi       = {10.1007/s11207-021-01771-y},
  pages     = {29},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}