Testing cosmological models using relative mass-redshift abundance of SZ clusters

TL;DR

This paper proposes using the relative abundance of galaxy clusters at different masses and redshifts to test cosmological models, specifically evaluating the standard LCDM model against SZ cluster observations with a simple efficiency parameterization.

Contribution

It introduces a method to test cosmological models using relative cluster abundance and a simple efficiency function, addressing observational biases.

Findings

Standard LCDM model is barely consistent with SPT cluster data.

A simple two-dimensional efficiency function can model detection biases.

More data are needed for definitive conclusions.

Abstract

Recent detection of high-redshift, massive clusters through Sunyaev-Zel'dovich observations has opened up a new way to test cosmological models. It is known that detection of a single supermassive cluster at a very high redshift can rule out many cosmological models all together. However, since dealing with different observational biases makes it difficult to test the likeliness of the data assuming a cosmological model, most of the cluster data (except those with high mass-redshift) stays untouched in confronting cosmological models with cluster observations. We propose here that one can use the relative abundance of the clusters with different masses at different redshifts to test the likeliness of the data in the context of cosmological models. For this purpose we propose a simple parametric form for the efficiency of observing clusters at different mass-redshift and we test if the standard LCDM model can explain the observed abundance of the clusters using this efficiency parameterization. We argue that one cannot expect an unusual and highly parametric form of the efficiency function to fit the observed data assuming a theoretical model. Using many realizations of Monte Carlo simulations we show that the standard spatially flat LCDM model is barely consistent with the SPT cluster data using a simple and plausible two-dimensional efficiency function for detection of the clusters. More cluster data are needed to make any strong conclusion.