论文标题
使用扩展数据的$λ$ CDM和WCDM宇宙学之间的高斯判别器
Gaussian discriminators between $Λ$CDM and wCDM cosmologies using expansion data
论文作者
论文摘要
高斯线性模型提供了一种独特的方法来分析后验概率分布以及贝叶斯证据。考虑到膨胀率数据，可以将高斯线性模型用于$λ$ CDM，WCDM和非燃料$λ$ CDM。在本文中，我们以各种精确度模拟扩展数据并获得贝叶斯证据，然后被用来区分模型。数据不确定性在（0.5,10）\％$中的$σ\范围内，并且已经考虑了两个不同的采样率。我们的结果表明，可以区分$ W = -1.02 $（或$ W = -0.98 $）的型号与$λ$ CDM $（W = -1）$，其中$σ= 0.5 \％$ $ $σ= 0.5 \％$不确定性数据的不确定性。最后，我们使用当前可用的扩展速率数据对MCMC和高斯线性模型进行参数推断，并比较结果。
The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. Considering the expansion rate data, the Gaussian linear model can be applied for $Λ$CDM, wCDM, and a non-flat $Λ$CDM. In this paper, we simulate the expansion data with various precision and obtain the Bayesian evidence, then it has been used to discriminate the models. The data uncertainty is in the range $σ\in(0.5,10)\%$ and two different sampling rates have been considered. Our results indicate that it is possible to discriminate $w=-1.02$ (or $w=-0.98$) model from the $Λ$CDM $(w=-1)$ with $σ=0.5\%$ uncertainty in expansion rate data. Finally, we perform a parameters inference in both the MCMC and Gaussian linear model, using currently available expansion rate data, and compare the results.
