Automated star/galaxy discrimination with neural networks

Details Automated star/galaxy discrimination with neural networks Automated star/galaxy discrimination with neural networks

Jan 1992

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1992aj....103..318o&link_type=abstract

Astronomical Journal (ISSN 0004-6256), vol. 103, Jan. 1992, p. 318-331. Research supported by University of Minnesota.

Astronomy and Astrophysics

Astronomy

95

Astrometry, Astronomical Catalogs, Galaxies, Neural Nets, Sky Surveys (Astronomy), Star Distribution, Classifying, Image Analysis, Optical Scanners

Scientific paper

The paper describes automated methods developed for the purpose of classifying images detected with the University of Minnesota Automated Plate Scanner (APS) for cataloging the first epoch Palomar Sky Survey. A novel automated image classification technique, using a neural network for distinguishing stars from nonstellar objects was developed where classifications into stellar and nonstellar categories are based upon a 14-element image parameter set. It is shown that the application of a neural network allows a large number of image parameters to be used simultaneously in a classification.

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