The SDSS Coadd: A Galaxy Photometric Redshift Catalog

Abstract

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.

16 pages, 13 figures, submitted to ApJ. Analysis updated to remove proprietary BOSS data comprising small fraction (8%) of original spectroscopic training set and erroneously included. Changes in results are small compared to the errors and the conclusions are unaffected. arXiv admin note: substantial text overlap with arXiv:0708.0030

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