Automated classification of IUE low dispersion spectra (I)

Details Automated classification of IUE low dispersion spectra (I) Automated classification of IUE low dispersion spectra (I)

1995-04-26

arxiv.org/abs/astro-ph/9504092v1

Astronomy and Astrophysics

Astrophysics

6 pages, 5 figures, uses standard Springer-Verlag Astronony and Astrophysics l-aa.sty macros version 3.0 and epsf.tex. The Pos

Scientific paper

Along the life of the IUE project, a large archive with spectral data has been generated, requiring automated classification methods to be analyzed in an objective form. Previous automated classification methods used with IUE spectra were based on multivariate statistics. In this paper, we compare two classification methods that can be directly applied to spectra in the archive: metric distance and artificial neural networks. These methods are used to classify IUE low-dispersion spectra of normal stars with spectral types ranging from O3 to G5. The classification based on artificial neural networks performs better than the metric distance, allowing the determination of the spectral classes with an accuracy of 1.1 spectral subclasses. KeyWords: data analysis, spectroscopic, fundamental parameters

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