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Session 105 - Galaxies: Counts & Kinematics.
Display session, Saturday, January 10
Exhibit Hall,
We present preliminary results from a three-color survey of selected high-latitude fields of the digitized Second Palomar Observatory Sky Survey (DPOSS). Innovative automated image classification techniques employing decision tree and artificial neural network pattern classifiers are used to establish catalogs of stars and galaxies to an approximate magnitude limit of g_J \leq 21 mag. 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''. A multi-dimensional analysis of these quantities is performed using neural network and decision tree algorithms in an effort to perfect a viable galaxy morphology classifier for the DPOSS material. The resultant multi-color photometric catalog is used to compute the galaxy number counts in 3 bands (photographic JFN calibrated to Gunn gri). These counts, based on a catalog of several million galaxies, are compared to predictions from non-evolving and evolving galaxy models. These results serve as a fiducial measure of galaxy number counts at intermediate flux levels, and 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.
The author(s) of this abstract have provided an email address for comments about the abstract: sco@poseidon.caltech.edu
