Accurate Two-dimensional Classification of Stellar Spectra with Artificial Neural Networks

Details Accurate Two-dimensional Classification of Stellar Spectra with Artificial Neural Networks Accurate Two-dimensional Classification of Stellar Spectra with Artificial Neural Networks

Oct 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997apj...487..847w&link_type=abstract

Astrophysical Journal v.487, p.847

Astronomy and Astrophysics

Astronomy

18

Infrared: Stars, Methods: Statistical, Stars: Fundamental Parameters, Techniques: Spectroscopic

Scientific paper

We present a solution to the long-standing problem of automatically classifying stellar spectra of all temperature and luminosity classes with the accuracy shown by expert human classifiers. We use the 15 Angstroms resolution near-infrared spectral classification system described by Torres-Dodgen & Weaver in 1993. Using the spectrum with no manual intervention except wavelength registration, artificial neural networks (ANNs) can classify these spectra with Morgan-Keenan types with an accuracy comparable to that obtained by human experts using 2 Angstroms resolution blue spectra, which is about 0.5 types (subclasses) in temperature and about 0.25 classes in luminosity. Accurate temperature classification requires a hierarchy of ANNs, while luminosity classification is most successful with a single ANN. We propose an architecture for a fully automatic classification system.

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