基于相关对数正态模型的贝叶斯CMB前景分离
Bayesian CMB foreground separation with a correlated log-normal model
摘要：
从多频率观测中提取前景和CMB图主要依赖于不同分量的不同频率行为。现有的贝叶斯方法还利用高斯先验的CMB的相关结构是由一个未知的角功率谱。我们认为这是自然的延伸，使用非平凡的先验也为前景组件。专注于弥漫银河前景，我们提出了一个对数正态模型，包括未知的空间相关性内的每个组件和交叉相关性之间的不同前景组件。我们目前在低分辨率的案例研究，证明了该模型的优越性能相比，所有组件的分析与平面先验。
英文摘要：
The extraction of foreground and CMB maps from multi-frequency observations relies mostly on the different frequency behavior of the different components. Existing Bayesian methods additionally make use of a Gaussian prior for the CMB whose correlation structure is described by an unknown angular power spectrum. We argue for the natural extension of this by using non-trivial priors also for the foreground components. Focusing on diffuse Galactic foregrounds, we propose a log-normal model including unknown spatial correlations within each component and cross-correlations between the different foreground components. We present case studies at low resolution that demonstrate the superior performance of this model when compared to an analysis with flat priors for all components.
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