An Analysis of the Stellar Mass Properties of Distant Galaxy Clusters From the Massive and Distant Clusters of WISE Survey (MaDCoWS)

Abstract

The epoch at redshift z ∼ 1 and above is an important one for the study of the evolution of galaxy clusters, as this is the epoch where star formation in clus- ter cores in quenched. Most wide-area cluster surveys at this redshift range select clusters using the Sunyaev-Zel’dovich (SZ) effect, identifying clusters through their hot intracluster medium (ICM). In this work, I use the complementary Massive and Distant Clusters of WISE (MaDCoWS) infrared-selected survey to measure stellar mass properties of galaxy clusters at high redshift. I present initial measurements of the stellar mass fractions (f⋆) for a sample of MaDCoWS clusters and compare them to the stellar mass fractions of SZ-selected clusters in a similar mass and redshift range from the South Pole Telescope (SPT)-SZ Survey. I do not find a significant difference in mean f⋆ between the two selection methods, though we do find an unexpectedly large range in f⋆ for the SZ-selected clusters. I also make shallow measurements of the composite m3.6 luminosity function (LF) for the MaDCoWS clusters and find similar results to other studies of clusters at or near our redshift range. Adding optical and deeper mid-infrared data, I also present more complete stellar mass measurements and deeper luminosity functions for a sample of MaD- CoWS clusters. Using SED-fitting of deep optical and mid-infrared photometry, I establish the membership of objects along the lines-of-sight to these clusters and calculate the stellar masses of member galaxies. The results follow a similar pattern of stellar mass fraction to the initial results, but higher values overall, and improved stellar mass errors. The LFs are even more improved, with the deeper data allowing me to fit both the characteristic magnitude and faint-end slope simultaneously. I found an evolution in these parameters consistent with passive evolution, but with more data needed to reduce the uncertainty.