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The Performance of Neural Networks in Astronomical Image Classification

Published online by Cambridge University Press:  26 July 2016

S.C. Odewahn
S.C. Odewahn*
Affiliation:
Department of Astronomy, University of Minnesota, Minneapolis, MN 55455 U.S.A.

Abstract

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The use of neural network pattern recognition techniques in the field of astronomy is reviewed. In assessing the quality of image recognition derived from this method particular attention is given to the problem of star/galaxy discrimination in large digital sky surveys. A two color survey of 9 fields of the first epoch Palomar Sky Survey, centered on the North Galactic Pole, has been performed with the Minnesota Automated Plate Scanner. A set of neural network image classifiers are used to automatically perform star/galaxy discrimination. We assess the efficiency of image classification and sample completeness through comparisons with a variety of independent studies of the NGP area.

Type
Part Five: Image Detection, Cataloguing and Classification
Information
Symposium - International Astronomical Union , Volume 161: Astronomy from Wide-Field Imaging , 1994 , pp. 235 - 241
Copyright
Copyright © Kluwer 1994 

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