The two-point correlation function and morphological segregation in the Optical Redshift Survey

Abstract

We study the clustering of galaxies in real and redshift space using the Optical Redshift Survey (ORS). We estimate the two-point correlation function in redshift space, ξ(s), for several subsamples of ORS, spanning nearly a factor of 30 in volume. We detect significant variations in ξ(s) among the subsamples covering small volumes. For volumes ≳(75h-1 Mpc)3, however, the ORS subsamples present very similar clustering patterns. Fits of the form ξ(s) = (s/s0)ys give best-fitting values in the range 1,5≤ys≤1.7 and 6.5≤s0≤8.8 h-1 Mpc for several samples extending to redshifts of 8000 km s-1. However, in several cases ξ(s) is not well described by a single power-law, rendering the best-fitting values quite sensitive to the interval in s adopted. We find significant differences in clustering between the diameter-limited and magnitude-limited ORS samples within a radius of 4000 km s-1 centred on the Local Group; ξ(s) is larger for the magnitude-limited sample than for the diameter-limited one. We interpret this as an indirect result of the morphological segregation coupled with differences in morphological mix. We split ORS into different morphological subsamples and confirm the existence of morphological segregation of galaxies out to scales of s ∼10 h-1 Mpc. Our results indicate that the relative bias factor between early- and late-type galaxies may be weakly dependent on scale. If real, this would suggest non-linear biasing. We also compute correlations as a function of radial and projected separations, ξ(rp, π), from which we derive the real-space correlation function, ξ(r). We obtain values 4.9≤r0≤7.3 h-1 Mpc and 1.5≤yr≤1.7 for various ORS samples. As before, these values depend strongly on the range in r adopted for the fit. The results obtained in real space confirm those found using ξ(s), i.e. in small volumes; magnitude-limited samples show larger clustering than do diameter-limited ones. There is no difference when large volumes are considered. Our results prove to be robust to adoption of different estimators of ξ(s) and to alternative methods of compensating for sampling selection effects.