Mathematics – Probability
Scientific paper
Oct 1982
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1982spie..331..465v&link_type=abstract
IN: Instrumentation in astronomy IV; Proceedings of the Fourth Conference, Tucson, AZ, March 8-10, 1982 (A83-31976 14-35). Belli
Mathematics
Probability
124
Astronomical Photometry, Bayes Theorem, Classifications, Image Analysis, Background Radiation, Photons, Probability Theory, Templates
Scientific paper
A new automated astronomical image classifier is described. The classifier is of the Bayesian type using maximum-likelihood template with Poisson noise. The method's advantages are that there is no need for an explicit galaxy model, it provides a continuous spectrum between totally unresolved objects and obviously diffuse resolved galaxies, and it can assign a probability to the classification. The continuous nature of the classifier allows identification of intermediate types such as stellar objects with faint nebulosity and galaxies with bright unresolved nuclei. The ability to assign a probability to each classification allows a determination of when the noise, plate quality, and scale of the images no longer gives a sensible division of stars and galaxies. Also the probability allows the weighting of objects in statistical studies relying on this separation. The method is applied to the catalog of 4-meter prime focus plates automatically reduced by the FOCAS system. It is compared with the hypersurface clustering classifier of Jarvis and Tyson.
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