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arXiv · 1111.6620

The SDSS Coadd: A Galaxy Photometric Redshift Catalog

Ribamar R. R. Reis·Marcelle Soares-Santos·James Annis·Scott Dodelson·Jiangang Hao·David Johnston·Jeffrey Kubo·Huan Lin·Hee-Jong Seo·Melanie Simet
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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.

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Ribamar R. R. Reis, Marcelle Soares-Santos, James Annis, Scott Dodelson, Jiangang Hao, David Johnston, Jeffrey Kubo, Huan Lin, Hee-Jong Seo, Melanie Simet. 2011-11-28. The SDSS Coadd: A Galaxy Photometric Redshift Catalog. https://doi.org/10.1088/0004-637x/747/1/59

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