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Photometric Redshifts | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
There are no photometic redshifts available for data releases 2 through 4
(DR2-DR4). Starting with DR5, there are two versions of photometric redshift
in the SDSS databases, in the Photoz and
Photoz2 tables respectively. The
algorithms for generating these are described below.
Photoz TableThis set of photometric redshift has been obtained with the template fitting method. Please also see this link for more detailed information about this method..The template fitting approach simply compares the expected colors of a galaxy (derived from template spectral energy distributions) with those observed for an individual galaxy. The standard scenario for template fitting is to take a small number of spectral templates T (e.g., E, Sbc, Scd, and Irr galaxies) and choose the best fit by optimizing the likelihood of the fit as a function of redshift, type, and luminosity p(z, T, L). Variations on this approach have been developed in the last few decades, including ones that use a continuous distribution of spectral templates, enabling the error function in redshift and type to be well defined. Since a representative set of photometrically calibrated spectra in the full wavelength range of the filters is not easy to obtain, we have used the empirical templates of Coleman Weedman and Wu extended with spectral synthesis models. These templates were adjusted to fit the calibrations (see Budavari et al. AJ 120 1588 (2000)) For more detailed information see Csabai et al. AJ 125 580 (2003) and references therein. The table contains the estimated redshift, the best matching template's spectral class, K-corrections and absolute magnitudes. There are also some parameters of the chi-square fitting. Caveats: The quality of photometric redshift estimation of faint objects (or to be prcise with large photometric errors) is weak. The "quality", "zErr" and "tErr" values are just estimates, they are not always reliable. For this estimation we have used galaxy templates for all objects. Except for a few misidentified galaxies which were categorized as star in the photopipeline, the values fornon-galaxies shouldn't be used.
Photoz2 TableThe photometric redshifts from the U. Chicago/Fermilab/NYU group (H. Oyaizu, M. Lima, C. Cunha, H. Lin, J. Frieman, and E. Sheldon) are calculated using a Neural Network method that is similar in implementation to that of Collister and Lahav (2004, PASP, 116, 345). The photo-z training and validation sets consist of over 551,000 unique spectroscopic redshifts matched to nearly 640,000 SDSS photometric measurements. These spectroscopic redshifts come from the SDSS as well as the deeper galaxy surveys 2SLAQ, CFRS, CNOC2, TKRS, and DEEP+DEEP2.We provide photo-z estimates for a sample of over 77.4 million DR6 primary objects, classified as galaxies by the SDSS PHOTO pipeline (TYPE = 3),
with dereddened model magnitude r < 22,
and which do not have any of the flags BRIGHT,
SATURATED, or SATUR_CENTER set.
Note that this is a significant
change in the input galaxy sample selection compared to the DR5
version of Photoz2.
Our data model is
Both the "CC2" and "D1" photo-z's are neural network based estimators. "D1" uses the galaxy magnitudes in the photo-z fit, while "CC2" uses only galaxy colors (i.e., only magnitude differences). Both methods also employ concentration indices (the ratio of PetroR50 and PetroR90). The "D1" estimator provides smaller photo-z errors than the "CC2" estimator, and is recommended for bright galaxies r < 20 to minimize the overall photo-z scatter and bias. However, for faint galaxies r > 20, we recommend "CC2" as it provides more accurate photo-z redshift distributions. If a single photo-z method is desired for simplicity, we also recommend "CC2" as the better overall photo-z estimator. Please see this link for a detailed comparison of the two methods, including performance metrics (photo-z errors and biases), quality plots, and photometric redshift vs. spectroscopic redshift distributions in different magnitude bins. The photo-z errors (1&sigma, or 68% confidence) are computed using an empirical "Nearest Neighbor Error" (NNE) method. NNE is a training set based method that associates similar errors to objects with similar magnitudes, and is found to accurately predict the photo-z error when the training set is representative. The photo-z "flag" value is set to 2 for fainter objects with r > 20, whose photo-z's have larger uncertainties and biases. Full details about the Photoz2 photometric redshifts
are available here and in Oyaizu et al. (2007), ApJ, submitted,
arXiv:0708.0030 [astro-ph].
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