Galaxy Zoo Morphology and Photometric Redshifts in the Sloan Digital Sky
  Survey
  It has recently been demonstrated that one can accurately derive galaxy
morphology from particular primary and secondary isophotal shape estimates in
the Sloan Digital Sky Survey imaging catalog. This was accomplished by applying
Machine Learning techniques to the Galaxy Zoo morphology catalog. Using the
broad bandpass photometry of the Sloan Digital Sky Survey in combination with
with precise knowledge of galaxy morphology should help in estimating more
accurate photometric redshifts for galaxies. Using the Galaxy Zoo separation
for spirals and ellipticals in combination with Sloan Digital Sky Survey
photometry we attempt to calculate photometric redshifts. In the best case we
find that the root mean square error for Luminous Red Galaxies classified as
ellipticals is as low as 0.0118. Given these promising results we believe
better photometric redshift estimates for all galaxies in the Sloan Digital Sky
Survey ($\sim$350 million) will be feasible if researchers can also leverage
their derived morphologies via Machine Learning. These initial results look to
be promising for those interested in estimating Weak-Lensing, Baryonic Acoustic
Oscillation, and other fields dependent upon accurate photometric redshifts.
