A comparative study of stellar spectra analysis with neural networks in transformed domains

  • Authors:

  • Diego Ordóñez;Carlos Dafonte;Minia Manteiga;Bernardino Arcay

  • Affiliations:

  • Information and Communications Technologies Department, Faculty of Computer Science, University of La Coruña, A Coruña, Spain;Information and Communications Technologies Department, Faculty of Computer Science, University of La Coruña, A Coruña, Spain;Department of Navigation and Earth Sciences, University of A Coruña, A Coruña, Spain;Information and Communications Technologies Department, Faculty of Computer Science, University of La Coruña, A Coruña, Spain

  • Venue:
  • IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
  • Year:
  • 2009

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Abstract

The main purpose of the GAIA mission of the European Space Agency (ESA) is to carry out a stereoscopic census of our galaxy and its environment. This task is based on measurements that will provide with unprecedented exactitude information on the astrometry (distance, movements, and spectral energy distribution) of approximately 1% of the objects in the milky way (109 objects). In the case of the brightest objects, essentially stars, spectra with intermediate resolution in the region of the infrared CaII triplet will be measured with a dedicated spectrograph, RVS (Radial Velocity Spectrometer). Stars can be characterized on the basis of their principal atmospheric parameters: effective temperature, gravity, metal content (general abundance of elements other than H and He), and their abundance of alpha elements (elements with Z22, α), which provide information on the physical environment in which the star was born. The goal of the present work is to study spectral parameterization by means of ANN: it determines the optimal domain for the ANNs performance, and proposes an adequate noise detection and filtering algorithm by considering simulated data (synthetic spectra) in the spectral region of the RVS.