The Efficacy of Galaxy Shape Parameters in Photometric Redshift
  Estimation: A Neural Network Approach
  We present a determination of the effects of including galaxy morphological
parameters in photometric redshift estimation with an artificial neural network
method. Neural networks, which recognize patterns in the information content of
data in an unbiased way, can be a useful estimator of the additional
information contained in extra parameters, such as those describing morphology,
if the input data are treated on an equal footing. We use imaging and five band
photometric magnitudes from the All-wavelength Extended Groth Strip
International Survey. It is shown that certain principal components of the
morphology information are correlated with galaxy type. However, we find that
for the data used the inclusion of morphological information does not have a
statistically significant benefit for photometric redshift estimation with the
techniques employed here. The inclusion of these parameters may result in a
trade-off between extra information and additional noise, with the additional
noise becoming more dominant as more parameters are added.
