Sign on

SAO/NASA ADS Astronomy Abstract Service

· Find Similar Abstracts (with default settings below)
· Citations to the Article (13) (Citation History)
· Refereed Citations to the Article
· Author Comments
· Also-Read Articles (Reads History)
·
· Translate This Page
Title:
Automatic spectral classification
Authors:
Kurtz, M. J.
Affiliation:
AA(Dartmouth Coll., Hanover, NH.)
Publication:
Ph.D. Thesis Dartmouth Coll., Hanover, NH.
Publication Date:
00/1982
Category:
Astronomy
Origin:
STI
NASA/STI Keywords:
CROSS CORRELATION, MULTIVARIATE STATISTICAL ANALYSIS, PATTERN RECOGNITION, CLASSIFICATIONS, DATA REDUCTION, LUMINOSITY, OPEN CLUSTERS, SPECTROPHOTOMETERS, SPECTROSCOPY
Bibliographic Code:
1982PhDT.........2K

Abstract

The use of cross correlation (C.C.), and techniques of Multivariate Analysis and Pattern Recognition in the classification of stellar spectra are presented using an array of spectroscopic standards observed at low (14A) resolution with a digital spectrophotometer. The HR diagram for the faint young open cluster Trumpler 1 was obtained. C.C. is first used to investigate several possible methods of data reduction. The C.C. classification obtained using the optimum reduction techniques obtains a poor luminosity classification, and for nonsupergiants in the spectral range BO-M2 a mean error of 2.2 subtypes. Using Principal Component Analysis it was possible to separate O stars and supergiants earlier than A5 from the rest of the sample, thus resolving a systematic error in the C.C. classification, and achieving a modicum of luminosity discrimination.
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