Bibcode
Navarro, S. G.; Corradi, R. L. M.; Mampaso, A.
Bibliographical reference
Astronomy and Astrophysics, Volume 538, id.A76
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2
2012
Journal
Citations
29
Refereed citations
23
Description
Context. As part of a project aimed at deriving extinction-distances for
thirty-five planetary nebulae, spectra of a few thousand stars were
analyzed to determine their spectral type and luminosity class.
Aims: We present here the automatic spectral classification process used
to classify stellar spectra. This system can be used to classify any
other stellar spectra with similar or higher signal-to-noise ratios. Methods: Spectral classification was performed using a system of
artificial neural networks that were trained with a set of line-strength
indices selected among the spectral lines most sensitive to temperature
and the best luminosity tracers. The training and validation processes
of the neural networks are discussed and the results of additional
validation probes, designed to ensure the accuracy of the spectral
classification, are presented. Results: Our system permits the
classification of stellar spectra of signal-to-noise ratio (S/N)
significantly lower than it is generally considered to be needed. For
S/N ≥ 20, a precision generally better than two spectral subtypes is
obtained. At S/N < 20, classification is still possible but has a
lower precision. Its potential to identify peculiar sources, such as
emission-line stars, is also recognized.
Based on observations obtained at the 4.2 m WHT telescope of the Isaac
Newton Group of Telescopes in the Spanish Observatorio del Roque de Los
Muchachos of the Instituto de Astrofísica de Canarias.
Related projects
Bipolar Nebulae
This project has three major objectives: 1) To determine the physico-chemical characteristics of bipolar planetary nebulae and symbiotic nebulae, to help understanding the origin of bipolarity and to test theoretical models, mainly models with binary central stars, aimed at explaining the observed morphology and kinematics. 2) To study the low
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