SDSS Coadd: A Galaxy Photometric Redshift Catalog.

摘要

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-zs and the Nearest Neighbor Error (NNE) method to estimate photo-z errors for (approx) 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 (approx) 89, 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 SDSS-III's Baryon Oscillation Spectroscopic Survey (BOSS), 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)(sub 68) = 0.036. After presenting our results and quality tests, we provide a short guide for users accessing the public data.