We present a Bayesian approach to modelling galaxy clusters using multi-frequency pointed observations from telescopes that exploit the Sunyaev--Zeldovich effect. We use the recently developed MultiNest technique (Feroz, Hobson & Bridges, 2008) to ex
plore 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.
The Arcminute Microkelvin Imager is a pair of interferometer arrays operating with six frequency channels spanning 13.9-18.2 GHz, with very high sensitivity to angular scales 30-10. The telescope is aimed principally at Sunyaev-Zeldovich imaging of c
lusters of galaxies. We discuss the design of the telescope and describe and explain its electronic and mechanical systems.
