Bayesian modelling of clusters of galaxies from multi-frequency pointed Sunyaev--Zel'dovich observations

TL;DR

This paper introduces a Bayesian method using MultiNest for analyzing galaxy clusters via multi-frequency Sunyaev--Zel'dovich observations, enabling efficient parameter estimation and model comparison in high-dimensional spaces.

Contribution

The paper presents a novel Bayesian approach with MultiNest for joint analysis of multi-frequency SZ data, including model comparison and robustness testing.

Findings

Successfully analyzed simulated data with multiple frequency channels

Demonstrated robust detection of clusters and non-detection in absence of clusters

Efficiently explored high-dimensional parameter spaces on a single processor

Abstract

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 (Feroz, Hobson & Bridges, 2008) 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.