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In Lima et al. we presented a new method for estimating the redshift distribution,   N ( z ) , of a photometric galaxy sample, using photometric observables and weighted sampling from a spectroscopic subsample of the data. In this paper, we extend this method and explore various applications of it, using both simulations and real data from the Sloan Digital Sky Survey (SDSS). In addition to estimating the redshift distribution for an entire sample, the weighting method enables accurate estimates of the redshift probability distribution,   p ( z ) , for  each  galaxy in a photometric sample. Use of   p ( z )  in cosmological analyses can substantially reduce biases associated with traditional photometric redshifts, in which a single redshift estimate is associated with each galaxy. The weighting procedure also naturally indicates which galaxies in the photometric sample are expected to have accurate redshift estimates, namely those that lie in regions of photometric‐observable space that are well sampled by the spectroscopic subsample. In addition to providing a method that has some advantages over standard photo‐ z  estimates, the weights method can also be used in conjunction  with  photo‐ z  estimates e.g. by providing improved estimation of   N ( z )  via deconvolution of   N ( z  phot )  and improved estimates of photo‐ z  scatter and bias. We present a publicly available   p ( z )  catalogue for ∼78 million SDSS DR7 galaxies.

Estimating the redshift distribution of photometric galaxy samples – II. Applications and tests of a new method