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S. C. Odewahn (Arizona State University), R. R. Gal (JHU), R. R. de Carvalho (ON/CNPq), S. G. Djorgovski, A. A. Mahabal, R. Brunner, P. Lopes (CTIO), DPOSS Team
A multi-color survey of high-latitude fields covering 5000 square degrees of the digitized Second Palomar Observatory Sky Survey (DPOSS) will contain on the order of 1 million galaxies. We discuss two methods used to perform automated image classification for star-galaxy separation in DPOSS. As a source of classifier training/testing data we employ an unprecedented 500-field collection of CCD photometry in the Gunn gri system obtained with the Palomar 60". We have trained artificial neural network (ANN) and decision tree image classifiers (DT) using images of ~4000 galaxies and ~3000 stars classified with FOCAS on 52 deep CCD images. We assess the systematic errors in our classifiers as a function of apparent magnitude. In order to model the loss of galaxies through misclassification and the contamination of our galaxy samples by misclassified stars, we compare the DPOSS ANN+DT image classifications to image data from 46 CCD fields on 21 POSS fields not used in the intial training/testing process. We assess these same functions in a more stringent manner by comparing classifications of DPOSS images common to different fields via the plate overlaps.
These tests are combined to derive analytic descriptions of sample incompleteness and contamination for future use in our assessment of multi-color galaxy number counts and the two-point angular correlation function. These data provide a fiducial measure of galaxy number counts at intermediate flux levels for use in related studies of the high redshift Universe made with HST.
This work was supported in part by a grant from the Norris Foundation.
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The author(s) of this abstract have provided an email address for comments about the abstract: stephen.odewahn@asu.edu