An Artificial Neural Network approach to classify SDSS stellar spectra

Details An Artificial Neural Network approach to classify SDSS stellar spectra An Artificial Neural Network approach to classify SDSS stellar spectra

Jul 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011an....332..597s&link_type=abstract

Astronomische Nachrichten, Vol.332, Issue 6, p.597

Astronomy and Astrophysics

Astronomy

Methods: Miscellaneous, Stars: Early-Type, Stars: Late-Type, Surveys

Scientific paper

We present a combined method to classify stellar spectra of the seventh data release (DR7) of the SDSS via an Artificial Neural Network (ANN), derive radial velocities and to estimate distances from an isochrone fitting technique. In total, we used 29 182 spectra of stars falling in the effective temperature range between 10 000 and 5500 K, including white dwarfs. The targets were selected on the basis of SDSS colours. We compare our results not only with the SEGUE Stellar Parameter Pipeline output, but also with already published values and find excellent agreement. With new and extensive data sets from all-sky ground based as well as satellite missions, our approach will become very important and efficient to analyse these information.

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