Preparing ESA Gaia mission: stellar spectra parameterization by neural networks in the near infrared

Details Preparing ESA Gaia mission: stellar spectra parameterization by neural networks in the near infrared Preparing ESA Gaia mission: stellar spectra parameterization by neural networks in the near infrared

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008lnea....3..225o&link_type=abstract

Lecture Notes and Essays in Astrophysics, vol. 3. Proceedings of the 3rd Symposium of the Astrophysics Group of the Spanish Roya

Astronomy and Astrophysics

Astrophysics

Stellar Spectroscopy, Stellar Parameterization, Gaia Mission, Artificial Neural Networks.

Scientific paper

The Gaia mission of the European Space Agency, foreseen to be operative at the beginning of 2012, will extend the Hipparcos legacy by carrying out a census of the Galaxy and providing accurate information about the composition and motion of its main components. Data handling and analysis of information regarding the complete sky down to magnitude 17-18 will be, with no doubt, a challenge for both Astrophysics and Computational Sciences. We present here our preliminary results on the on-going study about the automated derivation of stellar atmospheric parameters in the spectral region of the Gaia RVS (Radial velocity spectrometer) instrument. The use of artificial neural networks (ANN) trained with synthetic model spectra was the method selected for such automated derivation. Both direct stellar fluxes and Fourier transform moduli of them have been considered as inputs to train and test the ANN performance. It is shown that ANN represent a good approach to analyze and parameterize such a large dataset as the one expected from Gaia. Preliminary results achieved are comparable to those obtained by direct spectroscopic or spectrophotometric analysis with synthetic model atmospheres, being their accuracy highly dependent on the spectral signal to noise ratio.

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