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arXiv 0811.1199astro-ph
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根据多频点Sunyaev-Zel 'dovich观测星系团的贝叶斯模拟

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

F. Feroz, M. P. Hobson, J. T. L. Zwart, R. D. E. Saunders, K. J. B. Grainge

摘要:

我们提出了一个贝叶斯方法来建模星系团使用多频点观测望远镜,利用Sunyaev-Zel'dovich效应。我们使用最近开发的MultiNest技术(Feroz,Hobson & Bridges,2008)来探索高维参数空间,并计算贝叶斯证据。这允许鲁棒的参数估计以及模型比较。模拟Arcminute Microkelvin成像仪观测的集群,在主CMB信号的存在下,无线电点源(检测以及未解决的背景)和接收机噪声的测试表明,我们的算法是能够联合分析的数据从六个频率通道,采样后验空间的模型和计算贝叶斯证据非常有效地在一个单一的处理器。我们还说明了我们的检测过程的鲁棒性,将其应用到一个领域的射电源和原始CMB,但没有集群,并表明,确实没有集群被确定。我们的方法扩展到多频SZ调查数据中的多个集群的检测和建模将在未来的工作中描述。

英文摘要:

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.

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