@article{DinevaVermaGonzalezManriqueetal.2020,
  author    = {Dineva, Ekaterina Ivanova and Verma, Meetu and Gonzalez Manrique, Sergio Javier and Schwartz, Pavol and Denker, Carsten},
  title     = {Cloud model inversions of strong chromospheric absorption lines using principal component analysis},
  series = {Astronomische Nachrichten = Astronomical notes},
  volume    = {341},
  journal   = {Astronomische Nachrichten = Astronomical notes},
  number    = {1},
  publisher = {Wiley-VCH Verl.},
  address   = {Berlin},
  issn      = {0004-6337},
  doi       = {10.1002/asna.202013652},
  pages     = {64 -- 78},
  year      = {2020},
  abstract  = {High-resolution spectroscopy of strong chromospheric absorption lines delivers nowadays several millions of spectra per observing day, when using fast scanning devices to cover large regions on the solar surface. Therefore, fast and robust inversion schemes are needed to explore the large data volume. Cloud model (CM) inversions of the chromospheric H alpha line are commonly employed to investigate various solar features including filaments, prominences, surges, jets, mottles, and (macro-) spicules. The choice of the CM was governed by its intuitive description of complex chromospheric structures as clouds suspended above the solar surface by magnetic fields. This study is based on observations of active region NOAA 11126 in H alpha, which were obtained November 18-23, 2010 with the echelle spectrograph of the vacuum tower telescope at the Observatorio del Teide, Spain. Principal component analysis reduces the dimensionality of spectra and conditions noise-stripped spectra for CM inversions. Modeled H alpha intensity and contrast profiles as well as CM parameters are collected in a database, which facilitates efficient processing of the observed spectra. Physical maps are computed representing the line-core and continuum intensity, absolute contrast, equivalent width, and Doppler velocities, among others. Noise-free spectra expedite the analysis of bisectors. The data processing is evaluated in the context of "big data," in particular with respect to automatic classification of spectra.},
  language  = {en}
}