Search

We present the methodology and data behind the photometric redshift data base of the Sloan Digital Sky Survey (SDSS) Data Release 12. We adopt a hybrid technique, empirically estimating the redshift via local regression on a spectroscopic training set, then fitting a spectrum template to obtain K-corrections and absolute magnitudes. The SDSS spectroscopic catalogue was augmented with data from other, publicly available spectroscopic surveys to mitigate target selection effects. The training set is comprised of 1976 978 galaxies, and extends up to redshift z ≈ 0.8, with a useful coverage of up to z ≈ 0.6. We provide photometric redshifts and realistic error estimates for the 208 474 076 galaxies of the SDSS primary photometric catalogue. We achieve an average bias of Δznorm = 5.84 × 10-5, a standard deviation of σ(Δznorm) = 0.0205, and a 3σ outlier rate of Po = 4.11 per cent when cross-validating on our training set. The published redshift error estimates and photometric error classes enable the selection of galaxies with high-quality photometric redshifts. We also provide a supplementary error map that allows additional, sophisticated filtering of the data. [ABSTRACT FROM AUTHOR]

We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the sources' spectral energy distributions (SEDs). We identify and locate star-forming quiescent galaxies, and active galactic nuclei, as well as colour changes due to spectral features, such as the 4000Å break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, such as the need for a lower fraction of M-type stars in certain templates. We compare our clustering-redshift estimates to photometric redshifts and find these two independent estimators to be in good agreement at each limiting magnitude considered. Finally, we present the global clustering-redshift distribution of all Sloan extended sources, showing objects up to z ∼ 0.8. While the overall shape agrees with that inferred from photometric redshifts, the clustering-redshift technique results in a smoother distribution, with no indication of structure in redshift space suggested by the photometricredshift estimates (likely artefacts imprinted by their spectroscopic training set).We also infer a higher fraction of high-redshift objects. The mapping between the four observed colours and redshift can be used to estimate the redshift probability distribution function of individual galaxies. Thiswork is an initial step towards producing a general mapping between redshift and all available observables in the photometric space, including brightness, size, concentration, and ellipticity. [ABSTRACT FROM AUTHOR]