论文标题
特殊速度的宇宙学推断的概率框架
A probabilistic framework for cosmological inference of peculiar velocities
论文作者
论文摘要
我们提出了一个贝叶斯分层框架，用于针对特殊速度调查的原则数据分析管道，这使得明确地构成了从独立的距离距离指示器约束宇宙学参数的推理问题。我们演示了基于基于平面的调查的方法。我们方法的本质是与可观察物（例如，角度大小，表面亮度，红移等）紧密合作，我们通过与概率分布一起使用摘要统计数据。分层方法以几种方式改进了通常的分析。特别是，它允许在校准阶段对宇宙学的事先假设进行一致的分析。此外，在参数估计中正确解释了校准不确定性。在所有潜在变量上进行了新的，完全分析的后部边缘化的结果，我们希望在即将进行的调查中进行更多的原则分析。还给出了从基本平面数据得出的特殊速度的最大后验估计器。
We present a Bayesian hierarchical framework for a principled data analysis pipeline of peculiar velocity surveys, which makes explicit the inference problem of constraining cosmological parameters from redshift-independent distance indicators. We demonstrate our method for a Fundamental Plane-based survey. The essence of our approach is to work closely with observables (e.g. angular size, surface brightness, redshift, etc), through which we bypass the use of summary statistics by working with the probability distributions. The hierarchical approach improves upon the usual analysis in several ways. In particular, it allows a consistent analysis without having to make prior assumptions about cosmology during the calibration phase. Moreover, calibration uncertainties are correctly accounted for in parameter estimation. Results are presented for a new, fully analytic posterior marginalised over all latent variables, which we expect to allow for more principled analyses in upcoming surveys. A maximum a posteriori estimator is also given for peculiar velocities derived from Fundamental Plane data.
