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Title:
The morphologies of distant galaxies. 1: an automated classification system
Authors:
Abraham, Roberto G.; Valdes, Francisco; Yee, H. K. C.; van den Bergh, Sidney
Affiliation:
AA(Dominion Astrophysical Observatory, NRCC, Victoria, Canada), AB(National Optical Astronomy Observatories, Tucson, AZ), AC(University of Toronto, Toronto, Ontario, Canada), AD(Dominion Astrophysical Observatory, NRCC, Victoria, Canada)
Publication:
Astrophysical Journal, Part 1 (ISSN 0004-637X), vol. 432, no. 1, p. 75-90 (ApJ Homepage)
Publication Date:
09/1994
Category:
Astrophysics
Origin:
STI
NASA/STI Keywords:
Astronomical Models, Computerized Simulation, Galactic Clusters, Image Classification, Mathematical Models, Morphology, Sky Surveys (Astronomy), Astronomical Photometry, Atmospheric Effects, Atmospheric Turbulence, Charge Coupled Devices, Monte Carlo Method, Seeing (Astronomy)
DOI:
10.1086/174550
Bibliographic Code:
1994ApJ...432...75A

Abstract

We describe an automated morphological classification system that is useful for the study of faint objects detected on CCD frames. The fundamental parameter in our system is the central concentration of light. This parameter traces both the disk-to-bulge ratio and the effective radius of the galactic bulge component. We show that our classification system is less sensitive to seeing degradation than the Hubble system. The fundamental parameter of our classification system is of physical interest as a tracer of stellar population. In some respects our system might be regarded as an automated version of Morgan's Yerkes system. Our classification scheme is illustrated by using wide-field CCD data of the core of the galaxy cluster Abell 957. Many of the galaxies in the core of this cluster are poorly described by the Hubble system, but are well described by a classification system based on central concentration of light. We have also artificially redshifted our wide-field CCD image to mimic the appearance of an intermediate-redshift cluster seen under excellent seeing conditions, and show that our procedure makes it possible to morphologically classify faint galaxies out to z approximately 0.5 using ground-based data. Monte Carlo simulations are used to quantify the statistical uncertainties inherent in our morphological classifications.

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