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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 theArtificial Neural Network (ANN) technique to calculate photo-z's and theNearest Neighbor Error (NNE) method to estimate photo-z errors for ~ 77 millionobjects classified as galaxies in DR6 with r < 22. The photo-z and photo-zerror estimators are trained and validated on a sample of ~ 640,000 galaxiesthat 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 errorsmaller than sigma_{68} =0.021 or $0.024. After presenting our results andquality tests, we provide a short guide for users accessing the public data.

A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6