Photometric Redshift Estimation with Galaxy Morphology using
  Self-Organizing Maps
  We use multi-band optical and near-infrared photometric observations of
galaxies in the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey
(CANDELS) to predict photometric redshifts using artificial neural networks.
The multi-band observations span over 0.39 microns to 8.0 microns for a sample
of about 1000 galaxies in the GOODS-S field for which robust size measurements
are available from Hubble Space Telescope Wide Field Camera 3 observations. We
use Self Organizing Maps (SOMs) to map the multi dimensional photometric and
galaxy size observations while taking advantage of existing spectroscopic
redshifts at 0 < z < 2 for independent training and testing sets. We show that
use of photometric and morphological data led to redshift estimates comparable
to redshift measurements from SED modeling and from self-organizing maps
without morphological measurements.
