Angular Clustering with Photometric Redshifts in the Sloan Digital Sky Survey: Bimodality in the Clustering Properties of Galaxies

Understanding the clustering of galaxies has long been a goal of modern observational cosmology. Redshift surveys have been used to measure the correlation length as a function of luminosity and color. However, when subdividing the catalogs into multiple subsets, the errors increase rapidly. Angular clustering in magnitude-limited photometric surveys has the advantage of much larger catalogs but suffers from a dilution of the clustering signal because of the broad radial distribution of the sample. Also, up to now it has not been possible to select uniform subsamples based on physical parameters, such as luminosity and rest-frame color. Utilizing our photometric redshift technique, a volume-limited sample (0.1 < z < 0.3) containing more than 2 million galaxies is constructed from the Sloan Digital Sky Survey galaxy catalog. In the largest such analysis to date, we study the angular clustering as a function of luminosity and spectral type. Using Limber's equation, we calculate the clustering length for the full data set as r0 = 5.77 ± 0.10 h-1 Mpc. We find that r0 increases with luminosity by a factor of 1.6 over the sampled luminosity range, in agreement with previous redshift surveys. We also find that both the clustering length and the slope of the correlation function depend on the galaxy type. In particular, by splitting the galaxies in four groups by their rest-frame type, we find a bimodal behavior in their clustering properties. Galaxies with spectral types similar to elliptical galaxies have a correlation length of 6.59 ± 0.17 h-1 Mpc and a slope of the angular correlation function of 0.96 ± 0.05, while blue galaxies have a clustering length of 4.51 ± 0.19 h-1 Mpc and a slope of 0.68 ± 0.09. The two intermediate color groups behave like their more extreme "siblings" rather than showing a gradual transition in slope. We discuss these correlations in the context of current cosmological models for structure formation.