CMB Gibbs采样器的独立指南
A self-contained guide to the CMB Gibbs sampler
摘要：
我们提出了一个一致的自包含和教学审查的CMB吉布斯采样器，专注于计算方法和代码设计。我们提供了一个易于使用的CMB吉布斯采样器命名为SLAVE开发的C++使用面向对象的设计。在讨论为什么需要一个吉布斯采样器是显而易见的，什么样的吉布斯采样器可以用于在宇宙学的背景下，我们详细审查的条件概率密度的解析表达式，并讨论银河前景去除和各向异性噪声的问题。在证明了SLAVE是一个有效的、可用的CMB Gibbs采样器之后，我们提出了白噪声水平估计的算法。然后，在介绍使用Blackwell-Rao估计器获得最佳拟合功率谱的后处理实用程序之前，我们给出了操作SLAVE的简短指南。
英文摘要：
We present a consistent self-contained and pedagogical review of the CMB Gibbs sampler, focusing on computational methods and code design. We provide an easy-to-use CMB Gibbs sampler named SLAVE developed in C++ using object-oriented design. While discussing why the need for a Gibbs sampler is evident and what the Gibbs sampler can be used for in a cosmological context, we review in detail the analytical expressions for the conditional probability densities and discuss the problems of galactic foreground removal and anisotropic noise. Having demonstrated that SLAVE is a working, usable CMB Gibbs sampler, we present the algorithm for white noise level estimation. We then give a short guide on operating SLAVE before introducing the post-processing utilities for obtaining the best-fit power spectrum using the Blackwell-Rao estimator.
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