Cluster mass estimation through Fair Galaxies

TL;DR

This paper introduces a new method to estimate galaxy cluster masses using a subset of 'fair galaxies' identified through their position in redshift space, validated on simulations and SDSS data.

Contribution

The paper develops a novel technique for cluster mass estimation based on the infall velocity-overdensity relation and the concept of fair galaxies, applicable to large observational datasets.

Findings

Mass profiles estimated within 1-7 virial radii with ~1.5 uncertainty factor.

Technique effective on both simulated and real SDSS clusters.

Reliable even for sparsely populated clusters.

Abstract

We analyse a catalogue of simulated clusters within the theoretical framework of the Spherical Collapse Model (SCM), and demonstrate that the relation between the infall velocity of member galaxies and the cluster matter overdensity can be used to estimate the mass profile of clusters, even though we do not know the full dynamics of all the member galaxies. In fact, we are able to identify a limited subset of member galaxies, the 'fair galaxies', which are suitable for this purpose. The fair galaxies are identified within a particular region of the galaxy distribution in the redshift (line-of-sight velocity versus sky-plane distance from the cluster centre). This 'fair region' is unambiguously defined through statistical and geometrical assumptions based on the SCM. These results are used to develop a new technique for estimating the mass profiles of observed clusters and subsequently their masses. We tested our technique on a sample of simulated clusters; the mass profiles estimates are proved to be efficient from 1 up to 7 virialization radii, within a typical uncertainty factor of 1.5, for more than 90 per cent of the clusters considered. Moreover, as an example, we used our technique to estimate the mass profiles and the masses of some observed clusters of the Cluster Infall Regions in the Sloan Digital Sky Survey catalogue. The technique is shown to be reliable also when it is applied to sparse populated clusters. These characteristics make our technique suitable to be used in clusters of large observational catalogues.