Statistics – Computation
Scientific paper
Jan 1995
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995phdt.........1f&link_type=abstract
Thesis (OH.D.)--PRINCETON UNIVERSITY, 1995.Source: Dissertation Abstracts International, Volume: 56-02, Section: B, page: 0870.
Statistics
Computation
Hubble Classification, Photometry, Image Processing
Scientific paper
An automatic way of analyzing photometric images of galaxies and reducing parameters for the purpose of automatic morphological classification is presented here. The techniques are demonstrated on a set of nearby galaxies. A statistical analysis of the properties of these galaxies provides insights of the usefulness of the individual parameters for classification. A digital catalog of images of 113 nearby galaxies, developed for the purposes of this project, is presented in Chapter 1. These galaxies are all bright, large and well resolved. All images were recorded with charge coupled devices (CCDs). The galaxies were selected to span the Hubble classification classes. All foreground stars were removed and the data were photometrically calibrated. A new procedure, designed to remove foreground stars from galaxy profiles is detailed in Chapter 2. This method treats star removal from images more carefully than existing stellar photometry programs. An automatic star -remover is required to preserve the galaxy profile under the removed stars as reliably as possible in order to be able to derive parameters of the galaxy in later computations. Color-color relations for galaxies on several photometric systems as functions of redshift are presented in Chapter 3. These relations were calculated using galaxy energy distributions representing different Hubble types. The photometric systems were tied together using spectra of photometric standard stars. These results were used for the calibration of some of our catalog galaxies. The reduction of several photometric parameters from the images is described in Chapter 4. The performance of the automatic processing of images is demonstrated for artificial galaxies and for some real examples. A table with important parameters of the catalog galaxies is presented. Results of multivariate analysis of the data is given. Parameters are plotted against Hubble type and each other, correlations are pointed out, and useful quantities for classification are identified. Automatic classification of galaxies based on photometric images in large survey projects can be performed using the results presented here.
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