Sign on

SAO/NASA ADS Astronomy Abstract Service

· Find Similar Abstracts (with default settings below)
· Electronic Refereed Journal Article (HTML)
· Full Refereed Journal Article (PDF/Postscript)
· References in the article
· Citations to the Article (16) (Citation History)
· Refereed Citations to the Article
· SIMBAD Objects (261)
· Also-Read Articles (Reads History)
·
· Translate This Page
Title:
Accurate Two-dimensional Classification of Stellar Spectra with Artificial Neural Networks
Authors:
Weaver, Wm. Bruce; Torres-Dodgen, Ana V.
Publication:
Astrophysical Journal v.487, p.847 (ApJ Homepage)
Publication Date:
10/1997
Origin:
APJ
ApJ Keywords:
INFRARED: STARS, METHODS: STATISTICAL, STARS: FUNDAMENTAL PARAMETERS, TECHNIQUES: SPECTROSCOPIC
DOI:
10.1086/304651
Bibliographic Code:
1997ApJ...487..847W

Abstract

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.
Bibtex entry for this abstract   Preferred format for this abstract (see Preferences)
   

Find Similar Abstracts:

Use: Authors
Title
Keywords (in text query field)
Abstract Text
Return: Query Results Return    items starting with number
Query Form
Database: Astronomy
Physics
arXiv e-prints