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S. C. Odewahn, S. G. Djorgovski, R. J. Brunner, R. R. Gal (Caltech), R.R. de Carvalho (ON/CNPq, Brasil)
We present a three-color survey of selected high-latitude fields of the digitized Second Palomar Observatory Sky Survey (DPOSS).
Innovative automated image classification techniques employing artificial neural network and decision tree pattern classifiers are used to establish catalogs of stars and galaxies to an approximate magnitude limit of gJ \leq 21 mag. These classifiers have been established using training data drawn from over 550 Gunn gri CCD fields observed with the Palomar 60-inch. A variety of global photometric properties and Fourier image models are extracted from the JFN galaxy images having diameters measured at the 25 mag\ arcsec-2 isophote larger than 30'' and/or gJ \leq 18. A multi-dimensional analysis of these quantities is performed using artificial neural networks to develop a viable galaxy morphology classifier for the DPOSS material. We present a new morphological classifiction approach using Fourier image models to identify barred and ringed spiral systems.
The DPOSS multi-color photometric catalog is used to compute morphological galaxy number counts in 3 bands (photographic JFN calibrated to Gunn gri). These data will be compared to predictions from non-evolving and evolving galaxy models and serve as a fiducial measure of galaxy number counts at intermediate flux levels. The results are discussed in the context of related studies of the high redshift Universe made with the Keck telescopes and HST.
This work is supported in part by a grant from the Norris Foundation.