Dr Ian Lewis

Abstract:

We use the 2dF Galaxy Redshift Survey to measure the dependence of the bJ-band galaxy luminosity function on large-scale environment, defined by density contrast in spheres of radius 8h^-1 Mpc, and on spectral type, determined from principal component analysis. We find that the galaxy populations at both extremes of density differ significantly from that at the mean density. The population in voids is dominated by late types and shows, relative to the mean, a deficit of galaxies that becomes increasingly pronounced at magnitudes brighter than MbJ, - 5 log10 h ≲ -18.5. In contrast, cluster regions have a relative excess of very bright early-type galaxies with MbJ, - 5 log10 h ≲ -21. Differences in the mid- to faint-end population between environments are significant: at MbJ, -5 log10 h = -18 early- and late-type cluster galaxies show comparable abundances, whereas in voids the late types dominate by almost an order of magnitude. We find that the luminosity functions measured in all density environments, from voids to clusters, can be approximated by Schechter functions with parameters that vary smoothly with local density, but in a fashion that differs strikingly for early- and late-type galaxies. These observed variations, combined with our finding that the faint-end slope of the overall luminosity function depends at most weakly on density environment, may prove to be a significant challenge for models of galaxy formation.

Abstract:

We present an analysis of the relative bias between early- and late-type galaxies in the Two-degree Field Galaxy Redshift Survey (2dFGRS) - as defined by the η parameter of Madgwick et al., which quantifies the spectral type of galaxies in the survey. We calculate counts in cells for flux-limited samples of early- and late-type galaxies, using approximately cubical cells with sides ranging from 7 to 42 h-1 Mpc. We measure the variance of the counts in cells using the method of Efstathiou et al., which we find requires a correction for a finite volume effect equivalent to the integral constraint bias of the autocorrelation function. Using a maximum-likelihood technique we fit lognormal models to the one-point density distribution, and develop methods of dealing with biases in the recovered variances resulting from this technique. We then examine the joint density distribution function, f(δE, δL), and directly fit deterministic bias models to the joint counts in cells. We measure a linear relative bias of ≈1.3, which does not vary significantly with ℓ. A deterministic linear bias model is, however, a poor approximation to the data, especially on small scales (ℓ ≤ 28 h-1 Mpc) where deterministic linear bias is excluded at high significance. A power-law bias model with index b1 ≈ 0.75 is a significantly better fit to the data on all scales, although linear bias becomes consistent with the data for ℓ ≳ 40 h-1 Mpc.