The XMM Cluster Survey: X-ray analysis methodology
E. J. Lloyd-Davies, A. Kathy Romer, Nicola Mehrtens, Mark Hosmer,, Michael Davidson, Kivanc Sabirli, Robert G. Mann, Matt Hilton, Andrew R., Liddle, Pedro T. P. Viana, Heather C. Campbell, Chris A. Collins, E. Naomi, Dubois, Peter Freeman, Craig D. Harrison, Ben Hoyle

TL;DR
The paper details the data processing and analysis methods used in the XMM Cluster Survey to identify galaxy clusters, measure their properties, and assess the survey's selection function, enabling cosmological studies.
Contribution
It introduces automated pipelines for spectral and surface brightness fitting, redshift estimation from X-ray data, and validates cluster detection robustness using hydrodynamical simulations.
Findings
Detected 3,675 cluster candidates over 410 deg^2
Measured X-ray temperatures for 587 clusters with <40% accuracy
Demonstrated the robustness of detection algorithms across cluster morphologies
Abstract
The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5,776 XMM observations used to construct the current XCS source catalogue. A total of 3,675 > 4-sigma cluster candidates with > 50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg^2. Of these, 993 candidates are detected with > 300 background-subtracted X-ray photon counts, and we demonstrate that robust temperature measurements can be obtained down to this count limit. We describe in detail the automated pipelines used to perform the spectral and surface brightness fitting for these candidates,…
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