Serendipitous survey of galaxy clusters with XMM-Newton

Abstract

We present the first release of the XMM Cluster Survey (XCS), a serendipitous survey of clusters of galaxies using archival XMM-Newton data.

In this thesis we describe in detail the automated pipeline used to search the XMM images for extended sources. We also discuss techniques for the identification of clusters which are already known. We perform a rigorous set of tests designed to validate and quantify the effectiveness of the algorithms.

Furthermore, we have developed a methodology for describing quantitatively the survey selection function and include the implications for the XCS constraints on the cosmological parameters as δ₈ and Ωm.

We compile three main catalogues from the set of 63327 detected sources. The first is a statistically well-defined sample of 142 known clusters and 1622 new candidates. The second is a smaller list of 90 XCS cluster candidates identified in the non-statistical sample. Finally, we include for completeness the point sources detected in the survey. We also describe the database used to store and access the survey data and source lists.

Strategies for the long-term follow-up of the catalogues in the low, medium and high redshift regimes are investigated. We have measured photometric redshifts for 219 new candidate clusters out to z ~ 0.3

We conclude with the discoveries in the XCS of the most distant X-ray cluster currently known, at z = 1.5, and a likely supercluster at z = 0.9.