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Parameter Extraction from RVS Stellar Spectra by Means of Artificial Neural Networks and Spectral Density Analysis

  • Diego Ordóñez
  • Carlos Dafonte
  • Minia Manteiga
  • Bernardino Arcay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5271)

Abstract

The Gaia Galactic Survey Satellite is among the key future missions of the European Space Agency, who plans its launch near the end of 2011. Gaia will carry out what is being called a “Galaxy Survey”, gathering exact information on the nature of the galaxy’s main constituents, including the spectra of objects. A first approach to the extraction of atmospheric parameters from spectra (Effective Temperature (Teff), Gravity (log G), Metallicity ([Fe/H]), and Alpha elements abundance ([α/Fe])) was their indirect measurement by means of the Morgan-Keenan (MK) classification system. However, traditional methods based on MK are slow and subjective. This is why an automatic and robust method has become an essential requirement. This work presents the preliminary results of a study on the automatic extraction potential of the main atmospheric stellar parameters: Teff, log G, [Fe/H], and [α/Fe] in the spectral region of the RVS, between 8470 and 8740 Å. The study was carried out by means of an algorithm based on artificial neural networks which is a technique widely used in that kind of automatic spectral parameterisation.

Keywords

stellar spectra signal processing spectral density FFT SNR Artificial Neural Network feed-forward backpropagation wavelet 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Diego Ordóñez
    • 1
  • Carlos Dafonte
    • 1
  • Minia Manteiga
    • 2
  • Bernardino Arcay
    • 1
  1. 1.Information and Comunications Technologies Department, Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Department of Navigation and Earth SciencesUniversity of A CoruñaA CoruñaSpain

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