Galaxy colour, morphology and environment in the Sloan Digital Sky Survey

Abstract

We use the Fourth Data Release of the Sloan Digital Sky Survey to investigate the relation between galaxy rest‐frame u−r colour, morphology, as described by the concentration and Sérsic indices, and environmental density, for a sample of 79 553 galaxies at z≲ 0.1. We split the samples according to density and luminosity, and recover the expected bimodal distribution in the colour–morphology plane, shown especially clearly by this subsampling. We quantify the bimodality by a sum of two Gaussians on the colour and morphology axes, and show that, for the red‐/early‐type population both colour and morphology do not change significantly as a function of density. For the blue‐/late‐type population, with increasing density the colour becomes redder but the morphology again does not change significantly. Both populations become monotonically redder and of earlier type with increasing luminosity. There is no significant qualitative difference between the behaviour of the two morphological measures. Motivated by their long‐standing use in astronomy and their ability to utilize information not necessarily used by the concentration and Sérsic indices, we supplement the morphological sample with 13 655 galaxies assigned Hubble types by an artificial neural network. We find, however, that the resulting distribution is less well described by two Gaussians. Therefore, there are either more than two significant morphological populations, physical processes not seen in colour space, or the Hubble type, particularly the different subtypes of spirals Sa–Sd, has an irreducible fuzziness when related to environmental density. For each of the three measures of morphology, on removing the density relation due to it, we recover a strong residual relation in colour. However, on similarly removing the colour–density relation there is no evidence for a residual relation due to morphology. Therefore, either the morphology is not directly affected by the environmental density beyond the correlation to colour, or a single galaxy ‘type’ does not capture sufficient information.

VIMOS Ultra-Deep Survey (VUDS): Witnessing the assembly of a massive cluster atz~ 3.3

“…At lower redshift, massive clusters, like the one that Cl J0227-0421 is predicted to evolve into, show marked increases in the abundances of bright and massive redsequence galaxies (RSGs) relative to less dense environments (e.g., Ball et al 2008;Wetzel et al 2012). The origin of such galaxies is the subject of much debate, since it is unclear how early and through which processes such galaxies built up consider stellar masses and eventually quenched.…”

Galaxy properties in clusters: dependence on the environment and the cluster identification techniques

“…Finally, we use the Sérsic index n, taken from Blanton et al (2005a). The distribution of n shows a bimodal distribution (see for instance Ball et al 2008;and Driver et al 2006). The n separation is set at 2.5, the value that divides in equal parts the two distributions and corresponds to objects with approximately equal bulge and disk components.…”

Comparison of density estimation methods for astronomical datasets

“…Two applications of the estimated densities are the exploration of morphology-density relation (see, e.g., Dressler 1980;Baldry et al 2006, in the context of the concentration-density relation) and environmental effects on the color-magnitude relation (e.g., Balogh et al 2004;Baldry et al 2006;Ball et al 2008). We define the inverse concentration index as…”

“…This method is typically used in studies of the morphology-density relation and other observational studies of the relation between environment and galaxy properties (e.g., Dressler 1980;Balogh et al 2004;Baldry et al 2006;Ball et al 2008;Cowan & Ivezic 2008;Deng et al 2009, just to mention a few studies). The second and third methods are A&A 531, A114 (2011) both adaptive-kernel density estimators, where a kernel whose size adapts to local conditions (usually isotropically), depending on some criteria set before or iteratively during the estimation process, is used to smooth the point distribution so that typical densities can be estimated.…”