Automated Classification and Stellar Parameterization .

Details Automated Classification and Stellar Parameterization . Automated Classification and Stellar Parameterization .

Aug 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006iaujd..13e...4g&link_type=abstract

Exploiting Large Surveys for Galactic Astronomy, 26th meeting of the IAU, Joint Discussion 13, 22-23 August 2006, Prague, Czech

Astronomy and Astrophysics

Astronomy

Scientific paper

Different approaches for automated spectral classification are critically reviewed. We also summarize ANN based methods which would be very efficient in quick handling of the large volumes of data generated by different surveys. We have obtained medium resolution spectra for a large sample of stars using 2.3m telescope at VBO, Kavalur, India. Our sample contains uniform distribution of stars in temperature range 4000 to 8000K, log g range of 2.0 to 5.0 and [Fe/H] range of 0 to -3. We have explored the application of artificial neural network for parameterization of these stars. We have used a set of stars with well determined atmospheric parameters for training the networks for temperature, gravity and metallicity estimations. We use these trained network to estimate metallicities for a sample of metal-poor candidate stars.

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