Making the observational parsimonious richness a working mass proxy

TL;DR

This study demonstrates that richness, defined by the number of bright cluster galaxies, can serve as a highly reliable and low-scatter mass proxy for galaxy clusters across a broad redshift range, enabling cost-effective mass estimation.

Contribution

It establishes an almost scatterless, minimally evolving mass-richness scaling relation and identifies the optimal aperture for richness measurement, improving mass estimates from survey data.

Findings

Scatter between mass and richness is as small as the best mass proxies.

The optimal aperture for richness measurement minimizes scatter and is nearly constant across redshifts.

Predicted masses from richness have a total scatter of 0.16 dex with caustic masses.

Abstract

Richness, i.e., the number of bright cluster galaxies, is known to correlate with the cluster mass, however, to exploit it as mass proxy we need a way to estimate the aperture in which galaxies should be counted that minimizes the scatter between mass and richness. In this work, using a sample of 39 clusters with accurate caustic masses at 0.1<z<0.22, we first show that the scatter between mass and richness derived from survey data is negligibly small, as small as best mass proxies. The scatter turns out to be smaller than in some previous works and has a 90% upper limit of 0.05 dex in mass. The current sample, adjoining 76 additional clusters analyzed in previous works, establishes an almost scatterless, minimally evolving (if at all), mass-richness scaling in the redshift range 0.03<z<0.55. We then exploit this negligible scatter to derive the reference aperture to be used to compute richness and to predict the mass of cluster samples. These predicted masses have a total 0.16 dex scatter with caustic mass, about half of which is not intrinsic to the proxy, but related to the noisiness of the caustic masses used for test proxy performances. These results make richness-based masses of best quality and available for large samples at a low observational cost.