  We present and describe a catalog of galaxy photometric redshifts (photo-z's)
for the Sloan Digital Sky Survey (SDSS) Coadd Data. We use the Artificial
Neural Network (ANN) technique to calculate photo-z's and the Nearest Neighbor
Error (NNE) method to estimate photo-z errors for $\sim$ 13 million objects
classified as galaxies in the coadd with $r < 24.5$. The photo-z and photo-z
error estimators are trained and validated on a sample of $\sim 83,000$
galaxies that have SDSS photometry and spectroscopic redshifts measured by the
SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology
Field Galaxy Survey (CNOC2), the Deep Extragalactic Evolutionary Probe Data
Release 3(DEEP2 DR3), the VIsible imaging Multi-Object Spectrograph - Very
Large Telescope Deep Survey (VVDS) and the WiggleZ Dark Energy Survey. For the
best ANN methods we have tried, we find that 68% of the galaxies in the
validation set have a photo-z error smaller than $\sigma_{68} =0.031$. After
presenting our results and quality tests, we provide a short guide for users
accessing the public data.
