# public documents.sextractor_doc

## [/] [measure_astromwin.tex] - Diff between revs 25 and 29

Rev 25 Rev 29
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%\section{2D-model fitting}
%\section{2D-model fitting}
%\subsection{Star/galaxy separation}
%\subsection{Star/galaxy separation}
%With the local \index{PSF} PSF and a noise \index{mode} model in hand, one can easily derive an optimum
%With the local \index{PSF} PSF and a noise \index{mode} model in hand, one can easily derive an optimum
%star/galaxy classifier. The problem was first addressed by \cite{sebok:1979}
%star/galaxy classifier. The problem was first addressed by \cite{sebok:1979}
and \cite{valdes:1982}. If detections can be classified as either a star
%and \cite{valdes:1982}. If detections can be classified as either a star
%(s) or a galaxy (g), then the {\em a posteriori} probability for having a
%(s) or a galaxy (g), then the {\em a posteriori} probability for having a
%star, given the observed vector of pixel values $\vec{I}$ is given by the Bayes
%star, given the observed vector of pixel values $\vec{I}$ is given by the Bayes
%theorem:
%theorem:
%
%
%P(s|\vec{I}) = \frac{P(\vec{I}|s)P(s)}{P(\vec{I}|s)P(s)+p(\vec{I}|g)P(g)},
%P(s|\vec{I}) = \frac{P(\vec{I}|s)P(s)}{P(\vec{I}|s)P(s)+p(\vec{I}|g)P(g)},