The ESO Nearby Abell Cluster Survey. II. The Distribution of Velocity Dispersions of Rich Galaxy Clusters

TL;DR

This study combines extensive redshift data to analyze the distribution of galaxy cluster velocity dispersions, revealing intrinsic variability and aligning with X-ray temperature distributions, while highlighting biases related to richness.

Contribution

Introduces a new method for removing foreground and background galaxies to accurately determine cluster velocity dispersions in a large, volume-limited sample.

Findings

Distribution of velocity dispersions agrees with X-ray temperature distribution.

Bias against low dispersions below about 800 km/sec in richness-limited samples.

Intrinsic spread in velocity dispersion and richness relation.

Abstract

Summary - By combining the 5634 redshifts from the ESO Nearby Abell Cluster Survey (the ENACS) with another 1000 redshifts from the literature we are able to study the distribution of velocity dispersions for a volume-limited sample of 128 $R_{\rm ACO} \geq 1$ clusters, out to a redshift $z=0.1$, in a solid angle of 2.55 sr centered on the South Galactic Pole. In deriving velocity dispersions we apply a new, physically motivated method for removing fore- and background galaxies. We discuss in detail the completeness of the cluster sample for which we derive the distribution of cluster velocity dispersions. The large apparent spread between velocity dispersion and richness must be largely intrinsic. A consequence of the very broad relation between richness and velocity dispersion is that all cluster samples complete in richness are biased against low dispersions. For the richness limit of our sample this bias operates below about 800 km/sec. Above 800 km/s, our distribution of global velocity dispersions is free from systematic errors and biases, and agrees very well with the most recent determination of the distribution of cluster X-ray temperatures. Therefore, we conclude that X-ray temperature and velocity dispersion distributions statistically measure the same cluster property. We stress the need for model predictions that produce exactly the same information as provided by the observations, for a sample of model clusters with a richness limit that mimics that of the sample of observed clusters.