Testing the Halo Model Against the SDSS Photometric Survey

We present halo model predictions for the expected angular clustering and associated errors from the completed Sloan Digital Sky Survey (SDSS) photometric galaxy sample. These results are used to constrain halo model parameters under the assumption of a fixed LCDM cosmology using standard Fisher matrix techniques. Given the ability of the five-color SDSS photometry to separate galaxies into sub-populations by intrinsic color, we also use extensions of the standard halo model formalism to calculate the expected clustering of red and blue galaxy sub-populations as a further test of the galaxy evolution included in the semi-analytic methods for populating dark matter halos with galaxies. The extremely small sample variance and Poisson errors from the completed SDSS survey should result in very impressive constraints (~1-10%) on the halo model parameters for a simple magnitude-limited sample and should provide an extremely useful check on the behavior of current and future N-body simulations and semi-analytic techniques. We also show that similar constraints are possible using a narrow selection function, as would be possible using photometric redshifts, without making linear assumptions regarding the evolution of the underlying power spectra. In both cases, we explore the effects of uncertainty in the selection function on the resulting constraints and the degeneracies between various combinations of parameters.Comment: 16 pages, 17 figure

Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts

We investigate the potential and accuracy of clustering-based redshift estimation using the method proposed by M\'enard et al. (2013). This technique enables the inference of redshift distributions from measurements of the spatial clustering of arbitrary sources, using a set of reference objects for which redshifts are known. We apply it to a sample of spectroscopic galaxies from the Sloan Digital Sky Survey and show that, after carefully controlling the sampling efficiency over the sky, we can estimate redshift distributions with high accuracy. Probing the full colour space of the SDSS galaxies, we show that we can recover the corresponding mean redshifts with an accuracy ranging from $\delta$z=0.001 to 0.01. We indicate that this mapping can be used to infer the redshift probability distribution of a single galaxy. We show how the lack of information on the galaxy bias limits the accuracy of the inference and show comparisons between clustering redshifts and photometric redshifts for this dataset. This analysis demonstrates, using real data, that clustering-based redshift inference provides a powerful data-driven technique to explore the redshift distribution of arbitrary datasets, without any prior knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom

Exploring the SDSS Photometric Galaxies with Clustering Redshifts

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 (SED). We identify and locate star-forming, quiescent galaxies, and AGN, as well as colour changes due to spectral features, such as the 4000 \AA{} 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 photometric redshift estimates (likely artifacts 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. This work 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.Comment: 12 pages, 9 figures, accepted to MNRA