Bayesian modelling of clusters of galaxies from multifrequency-pointed Sunyaev–Zel'dovich observations

• Published in: Monthly notices of the Royal Astronomical Society Vol. 398; no. 4; pp. 2049 - 2060
• Main Authors: Feroz, Farhan, Hobson, Michael P., Zwart, Jonathan T. L., Saunders, Richard D. E., Grainge, Keith J. B.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.10.2009; Wiley-Blackwell; Oxford University Press
• Subjects: Astronomy; Bayesian analysis; Cosmic background radiation; cosmic microwave background; cosmology: observations; Earth, ocean, space; Exact sciences and technology; galaxies: clusters: general; methods: data analysis; methods: statistical; Stars & galaxies; cosmic microwave background; methods: statistical; galaxies: clusters: general; cosmology: observations; methods: data analysis; Data analysis; Parameter estimation; Cosmic background radiation; Galaxy clusters; Statistical method; Algorithms; Sunyaev Zeldovich effect; Cosmic radio sources; Cosmology; Models; Point radiosource
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1111/j.1365-2966.2009.15247.x

We present a Bayesian approach to modelling galaxy clusters using multi-frequency pointed observations from telescopes that exploit the Sunyaev–Zel'dovich effect. We use the recently developed multinest technique to explore the high-dimensional parameter spaces and also to calculate the Bayesian evidence. This permits robust parameter estimation as well as model comparison. Tests on simulated Arcminute Microkelvin Imager observations of a cluster, in the presence of primary CMB signal, radio point sources (detected as well as an unresolved background) and receiver noise, show that our algorithm is able to analyse jointly the data from six frequency channels, sample the posterior space of the model and calculate the Bayesian evidence very efficiently on a single processor. We also illustrate the robustness of our detection process by applying it to a field with radio sources and primordial CMB but no cluster, and show that indeed no cluster is identified. The extension of our methodology to the detection and modelling of multiple clusters in multi-frequency SZ survey data will be described in a future work.