The distribution of red and blue galaxies in groups: an empirical test of the halo model

The popular halo model predicts that the power spectrum of the galaxy fluctuations is simply the sum of the large scale linear halo-halo power spectrum and the weighted power spectrum of the halo profile. Previous studies have derived halo parameters from the observed galaxy correlation function. Here we test the halo model directly for self-consistency with a minimal set of theoretical assumptions by utilising the 2dF Galaxy Redshift Survey (2dFGRS). We derive empirically the halo occupation and galaxy radial distributions in the haloes of the 2dF Percolation-Inferred Galaxy Group (2PIGG) catalogue. The mean halo occupation number is found to be well-fitted by a power-law, ~ M^b, at high masses, with b = 1.05, 0.88, 0.99 for red, blue and all galaxies respectively (with 1-sigma errors of 15-19%). We find that the truncated NFW profile provides a good fit to the galaxy radial distributions, with concentration parameters c=3.9, 1.3, 2.4 for red, blue and all galaxies respectively (with 1-sigma errors of 8-15%). Adding the observed linear power spectrum to these results, we compare these empirical predictions of the halo model with the observed correlation functions for these same 2dF galaxy populations. We conclude that subject to some fine tuning it is an acceptable model for the two-point correlations. Our analysis also explains why the correlation function slope of the red galaxies is steeper than that of the blue galaxies. It is mainly due to the number of red and blue galaxies per halo, rather than the radial distribution within the haloes of the two galaxy species.Comment: 15 pages, 15 figures. MNRAS accepted version. Adds appx. on profile fitting; now use truncated NF

ANNz: estimating photometric redshifts using artificial neural networks

We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the r.m.s. redshift error in the range 0 < z < 0.7 is 0.023. Non-ideal conditions (spectroscopic sets which are small, or which are brighter than the photometric set for which redshifts are required) are simulated and the impact on the photometric redshift accuracy assessed.Comment: 6 pages, 6 figures. Replaced to match version accepted by PASP (minor changes to original submission). The ANNz package may be obtained from http://www.ast.cam.ac.uk/~aa