Cluster Cosmology

Abstract

Galaxy clusters contain a wealth of information about the matter density of our universe. Measuring the density of clusters as a function of redshift and mass can tightly constrain the mass density parameter of the universe, Ωm. This constraint depends on a calibration of the mass-observable relations (MOR) and weak lensing. The standard model of cosmology ΛCDM is defined by six independent parameters. Cosmological analyses involve constraining some or all of these parameters. The Dark Energy Survey (DES) utilizes optical images of the sky to catalog the galaxies and then to constrain cosmological parameters and explore the nature of dark matter and dark energy of the universe. The DES relies on the 4-meter Blanco telescope in Chile and the DECam camera to collect its data. The images are analyzed to produce galaxy catalogs. The catalogs are then processed through a cluster of galaxies finding algorithm, of which the most prominent is redMaPPer [1]. The space density of the clusters as a function of their mass are then utilized to constrain the cosmology by comparison to simulated and theoretical models. Since the mass of clusters is not an observable, it is necessary to find an observable that correlates with mass. One of the most common is the or the count of red galaxies in a galaxy cluster, λ. The count of galaxies, richness, has been used in cluster catalogs since the first one [1] due to the simplicity of acquiring an accurate count. In modern surveys, weak lensing is used to calibrate the richness to a mass. Light curves when it passes through curved space due to the presence of massive objects, creating a lensing signal. This signal is highly sensitive to the mass of the cluster members which makes it ideal for the calibration of mass observable relation. In 2020, the DES cluster cosmology group unblinded their cosmological analysis. The results found differed from the prior value of Ωm. The DES collaboration believes systematics to be the cause of the discrepancy. In this project, we implemented a model of one of the systematics, the boost factor model. Details of the experiment are discussed in section 2. In section 3, the systematics necessary to the analysis are presented. Section 4 develops the Boost factor model implemented and the framework used to constrain the free parameters of the model. The goal of the project was to constrain the model parameters using MCMC