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arXiv 0709.1058astro-ph
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贝叶斯分量分离与CMB功率谱联合估计

Joint Bayesian component separation and CMB power spectrum estimation

H. K. Eriksen, J. B. Jewell, C. Dickinson, A. J. Banday, K. M. Gorski, C. R. Lawrence

摘要:

我们描述并实现了一个准确的,灵活的,计算效率高的算法,联合组件分离和CMB功率谱估计,建立在吉布斯采样框架。两个基本的新特征是1)前景光谱参数的条件采样,以及2)所有振幅型自由度(例如,CMB、前景像素幅度和全局模板幅度)。给定前景信号的参数模型,我们有效地和准确地估计准确的联合前景CMB后验分布,因此,所有的边缘分布,如CMB功率谱或前景谱指数后验。当前实施的主要限制是在所有频率下要求相同的光束响应,这将分析限制在给定实验的最低分辨率。我们概述了未来的推广多分辨率观测。为了验证该方法,我们分析了简单的模型,并将结果与分析预测进行了比较。然后,我们分析了一个现实的模拟与属性类似的3年WMAP数据,降级到一个共同的分辨率为3度半高宽。实际的3年WMAP温度分析结果在附函中给出。

英文摘要:

We describe and implement an exact, flexible, and computationally efficient algorithm for joint component separation and CMB power spectrum estimation, building on a Gibbs sampling framework. Two essential new features are 1) conditional sampling of foreground spectral parameters, and 2) joint sampling of all amplitude-type degrees of freedom (e.g., CMB, foreground pixel amplitudes, and global template amplitudes) given spectral parameters. Given a parametric model of the foreground signals, we estimate efficiently and accurately the exact joint foreground-CMB posterior distribution, and therefore all marginal distributions such as the CMB power spectrum or foreground spectral index posteriors. The main limitation of the current implementation is the requirement of identical beam responses at all frequencies, which restricts the analysis to the lowest resolution of a given experiment. We outline a future generalization to multi-resolution observations. To verify the method, we analyse simple models and compare the results to analytical predictions. We then analyze a realistic simulation with properties similar to the 3-yr WMAP data, downgraded to a common resolution of 3 degree FWHM. The results from the actual 3-yr WMAP temperature analysis are presented in a companion Letter.

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