The SDSS Coadd: A Galaxy Photometric Redshift Catalog
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 {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.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1033685
- Report Number(s):
- FERMILAB-PUB-11-628-A-AE-CD-PPD; arXiv eprint number arXiv:1111.6620; TRN: US1200601
- Country of Publication:
- United States
- Language:
- English
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