Optimal classification of images into stars or galaxies - A Bayesian approach

Astronomy and Astrophysics – Astronomy

Scientific paper

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Astronomical Photography, Bayes Theorem, Classifications, Galaxies, Stars, Luminous Intensity, Telescopes

Scientific paper

Bayesian decision theory is applied to the star/galaxy classification problem and a classifier is derived. This classifier can be written in the form of a correlation with a single stored function. The classifier is then applied to images on 48-in Schmidt plates. There are no free parameters which need to be adjusted for each plate. Calibration of the classifier to a plate simply involves obtaining images of (brighter) objects known to be stars. Sources of error are discussed and a second classifier, which is insensitive to variations in the sky background, is derived and applied to the plate data. Finally a prediction of the magnitude limit is derived for both classifiers and compared to the observed magnitude limit. This observed magnitude limit is about one to one and a half magnitudes above the plate detection limit.

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