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arXiv 2202.00962astro-ph.COgr-qc
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倾斜宇宙中减速参数的观测约束

Observational constraints on the deceleration parameter in a tilted universe

Kerkyra Asvesta, Lavrentios Kazantzidis, Leandros Perivolaropoulos, Christos G. Tsagas

摘要:

我们研究了一个参数化的减速参数在一个倾斜的宇宙,即宇宙模型配备了两个家庭的观察员。第一个家庭遵循光滑的哈勃流,而第二个是真正的观察者居住在一个典型的星系内的大规模流动和移动相对于光滑的哈勃膨胀有限的特殊速度。我们使用编译Ia型超新星(SnIa)的数据,如万神殿数据集中所描述的,找到适合的数据的质量和研究的减速参数的红移演化。在这样做时,我们考虑两种替代方案,假设大流量观察员生活在$Λ$ CDM和爱因斯坦-德西特宇宙。我们表明,倾斜的爱因斯坦-德西特模型可以重现宇宙的最近的加速历史,而不需要一个宇宙学常数或暗能量,通过简单地考虑特殊运动的线性效应。通过马尔可夫链蒙特卡罗(MCMC)方法,我们还约束的大小和两个模型的参数的不确定性。从我们的统计分析,我们发现,倾斜的爱因斯坦-德西特模型,配备了一个或两个额外的参数,描述了假设的大规模速度流,执行类似的标准$Λ$ CDM范式的模型选择标准(赤池信息准则和贝叶斯信息准则)的背景下。

英文摘要:

We study a parametrization of the deceleration parameter in a tilted universe, namely a cosmological model equipped with two families of observers. The first family follows the smooth Hubble flow, while the second are the real observers residing in a typical galaxy inside a bulk flow and moving relative to the smooth Hubble expansion with finite peculiar velocity. We use the compilation of Type Ia Supernovae (SnIa) data, as described in the Pantheon dataset, to find the quality of fit to the data and study the redshift evolution of the deceleration parameter. In so doing, we consider two alternative scenarios, assuming that the bulk-flow observers live in the $Λ$CDM and in the Einstein-de Sitter universe. We show that a tilted Einstein-de Sitter model can reproduce the recent acceleration history of the universe, without the need of a cosmological constant or dark energy, by simply taking into account linear effects of peculiar motions. By means of a Markov Chain Monte Carlo (MCMC) method, we also constrain the magnitude and the uncertainties of the parameters of the two models. From our statistical analysis, we find that the tilted Einstein-de Sitter model, equipped with one or two additional parameters that describe the assumed large-scale velocity flows, performs similar to the standard $Λ$CDM paradigm in the context of model selection criteria (Akaike Information Criterion and Bayesian Information Criterion).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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