论文标题
贝叶斯机器学习对宇宙不透明度的约束：$ h_ {0} $张力问题的隐藏侧
Constraints on Cosmic Opacity from Bayesian Machine Learning: The hidden side of the $H_{0}$ tension problem
论文作者
论文摘要
贝叶斯（概率）机器学习用于探测宇宙的不透明度。它依赖于生成过程，其中模型是生成涉及模型未知参数的数据，我们先前的信念，并允许我们获得后验结果。对宇宙不透明度的约束是针对两种平面型号（$λ$ cdm和xcdm（具有$ω__{de} \ neq -1 $）的两种平面型号的约束，用于三个红移范围，$ z \ in [0,2.5] $，$ z \ in [0,5] $ in [0,5] $，$ z \ in [0,5] $，$ z \ in [0,0,0,10 $ n，10] $，这是为了了解宇宙不透明度的约束如何在非常深的宇宙中改变，并检查在多大程度上存在红移范围的依赖性。不透明度的以下表单，$τ（z）=2εz$和$τ（z）=（1+z）^{2ε} -1 $，对应于$ z = 0 $的观察者，而$ z $的源为$ z $。我们的分析结果表明，宇宙不是完全透明的，这可能会对$ h_ {0} $张力问题产生重大影响。在生成过程中，由于宇宙不透明的原因，观察者收到的助焊剂已被考虑在内。在分析中，已经采用了与宇宙学模型相关的光度距离。
Bayesian (Probabilistic) Machine Learning is used to probe the opacity of the Universe. It relies on a generative process where the model is the key object to generate the data involving the unknown parameters of the model, our prior beliefs, and allows us to get the posterior results. The constraints on the cosmic opacity are determined for two flat models, $Λ$CDM and XCDM (this having $ω_{de} \neq -1$), for three redshift ranges, $z\in[0,2.5]$, $z \in[0,5]$, and $z\in[0,10]$, in each case. This is to understand how the constraints on the cosmic opacity could change in the very deep Universe, and also to check to what extent there is a redshift-range dependence. The following forms for the opacity, $τ(z) = 2εz$ and $τ(z) = (1+z)^{2ε} -1$, corresponding to an observer at $z=0$ and a source at $z$, are considered. The results of our analysis show that the Universe is not fully transparent, and this may have a significant impact on the $H_{0}$ tension problem. In the generative process, the fact that, owing to cosmic opacity, the flux received by the observer is reduced has been taken into account. In the analysis, the luminosity distance associated with the cosmological model has been employed.
