We present and describe a catalog of galaxy photometric redshifts (photo-z's)
for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). 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 ~ 77 million
objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z
error estimators are trained and validated on a sample of ~ 640,000 galaxies
that have SDSS photometry and spectroscopic redshifts measured by SDSS, 2SLAQ,
CFRS, CNOC2, TKRS, DEEP, and DEEP2. For the two 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} =0.021 or $0.024. After presenting our results and
quality tests, we provide a short guide for users accessing the public data.Comment: 16 pages, 12 figure