Cosmological baryonic and matter densities from 600 000 SDSS luminous red galaxies with photometric redshifts

We analyse MegaZ-LRG, a photometric-redshift catalogue of luminous red galaxies (LRGs) based on the imaging data of the Sloan Digital Sky Survey (SDSS) 4th Data Release. MegaZ-LRG, presented in a companion paper, contains > 10(6) photometric redshifts derived with ANNZ, an artificial neural network method, constrained by a spectroscopic subsample of approximate to 13 000 galaxies obtained by the 2dF-SDSS LRG and Quasar (2SLAQ) survey. The catalogue spans the redshift range 0.4 < z < 0.7 with an rms redshift error sigma(z) approximate to 0.03(1 + z), covering 5914 deg(2) to map out a total cosmic volume 2.5 h(-3) Gpc(3). In this study we use the most reliable 600 000 photometric redshifts to measure the large-scale structure using two methods: (1) a spherical harmonic analysis in redshift slices, and (2) a direct re-construction of the spatial clustering pattern using Fourier techniques. We present the first cosmological parameter fits to galaxy angular power spectra from a photometric-redshift survey. Combining the redshift slices with appropriate covariances, we determine best-fitting values for the matter density Omega(m) and baryon density Omega(b) of Omega(m)h = 0.195 +/- 0.023 and Omega(b)/Omega(m) = 0.16 +/- 0.036 (with the Hubble parameter h = 0.75 and scalar index of primordial fluctuations n(scalar) = 1 held fixed). These results are in agreement with and independent of the latest studies of the cosmic microwave background radiation, and their precision is comparable to analyses of contemporary spectroscopic-redshift surveys. We perform an extensive series of tests which conclude that our power spectrum measurements are robust against potential systematic photometric errors in the catalogue. We conclude that photometric-redshift surveys are competitive with spectroscopic surveys for measuring cosmological parameters in the simplest 'vanilla' models. Future deep imaging surveys have great potential for further improvement, provided that systematic errors can be controlled.