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We present redshift probability distributions for galaxies in the SDSS DR8imaging data. We used the nearest-neighbor weighting algorithm presented inLima et al. 2008 and Cunha et al. 2009 to derive the ensemble redshiftdistribution N(z), and individual redshift probability distributions P(z) forgalaxies with r < 21.8. As part of this technique, we calculated weights for aset of training galaxies with known redshifts such that their densitydistribution in five dimensional color-magnitude space was proportional to thatof the photometry-only sample, producing a nearly fair sample in that space. Wethen estimated the ensemble N(z) of the photometric sample by constructing aweighted histogram of the training set redshifts. We derived P(z) s forindividual objects using the same technique, but limiting to training setobjects from the local color-magnitude space around each photometric object.Using the P(z) for each galaxy, rather than an ensemble N(z), can reduce thestatistical error in measurements that depend on the redshifts of individualgalaxies. The spectroscopic training sample is substantially larger than thatused for the DR7 release, and the newly added PRIMUS catalog is now the mostimportant training set used in this analysis by a wide margin. We expect theprimary source of error in the N(z) reconstruction is sample variance: thetraining sets are drawn from relatively small volumes of space. Usingsimulations we estimated the uncertainty in N(z) at a given redshift is 10-15%.The uncertainty on calculations incorporating N(z) or P(z) depends on how theyare used; we discuss the case of weak lensing measurements. The P(z) catalog ispublicly available from the SDSS website.

Photometric Redshift Probability Distributions for Galaxies in the SDSS DR8