Abstract
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 neural network (NN) 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, the Two Degree Field, the SDSS Luminous Red Galaxy and Quasi-stellar Object Survey (2SLAQ), the Canada-France Redshift Survey (CFRS), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Team Keck Redshift Survey (TKRS), the Deep Extragalactic Evolutionary Probe (DEEP), and DEEP2. For the two best NN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than σ68 = 0.021 or 0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.