论文标题

倾斜宇宙中减速参数的观察性约束

Observational constraints on the deceleration parameter in a tilted universe

论文作者

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

论文摘要

我们研究了倾斜宇宙中减速参数的参数化,即配备两个观察者家族的宇宙学模型。第一个家庭遵循光滑的哈勃流动,而第二个家庭是驻留在散装流动中的典型星系中的真实观察者,并相对于光滑的哈勃膨胀,具有有限的特殊速度。如Pantheon数据集所述,我们使用IA型超新星(SNIA)数据的汇编来找到数据拟合的质量并研究减速参数的红移演化。这样一来,我们考虑了两个替代方案,假设散装观察者生活在$λ$ CDM和Einstein-De Sitter宇宙中。我们表明,一个倾斜的爱因斯坦 - 戴式保姆模型可以通过简单地考虑到特殊运动的线性效应,而无需宇宙学恒定或暗能量,而无需宇宙恒定或暗能量。通过马尔可夫链蒙特卡洛(MCMC)方法,我们还限制了两个模型参数的大小和不确定性。从我们的统计分析中,我们发现,倾斜的爱因斯坦-DE保姆模型配备了一个或两个附加参数,这些参数描述了假定的大规模速度流,在模型选择标准(Akaike Information Criteriation Criterion和Bayesian Information Information Information Information Informition)中,执行与标准$λ$ CDM Paradigm相似。

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).

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