论文标题
作为$λ$ CDM型号的扩展的散装粘度宇宙学的新参数化
A new Parametrization for Bulk Viscosity Cosmology as Extension of the $Λ$CDM Model
论文作者
论文摘要
与$λ$ CDM模型相比,冷暗物质中的散装粘度是一个吸引人的特征,它在宇宙学环境中引入了独特的现象学效应。在此观点下,我们提出了形式的块状粘度的一般参数化,$ξ\ sim h^{1-2S}ρ_{m}^{s} $,涵盖了Eckart理论中一些众所周知的案例。这种新的参数化的一些优点是:首先,它允许以自主系统的形式编写所得的宇宙学演化方程,以任何$ s $的值的形式,因此可以完成对固定点和稳定性的一般处理,其次,散装粘度效应始终如一地处理,以便在重要的密度密度散开时自然关闭。作为主要结果,我们发现,基于详细的动力学系统分析,在不同宇宙学期间,具有非呈现的散装粘度系数的De-sitter样渐近溶液的单参数家族。与分析相空间分析共同执行数值计算,以便更定量地评估构成粘度对宇宙学背景进化的影响。最后,作为与观察的首次接触,我们通过执行贝叶斯统计分析认为Markov Chain Chain Carlo Monte Monte Monte Monte Monte Monte Monte Monte Monte Monte Monte Monte方法,对具有特定$ S $ exponents的某些散装粘度模型的自由参数产生了约束。
Bulk viscosity in cold dark matter is an appealing feature that introduces distinctive phenomenological effects in the cosmological setting as compared to the $Λ$CDM model. Under this view, we propose a general parametrization of the bulk viscosity of the form $ξ\sim H^{1-2s} ρ_{m}^{s}$, that covers intriguingly some well-known cases in the Eckart's theory. Some advantages of this novel parametrization are: first, it allows to write the resulting equations of cosmological evolution in the form of an autonomous system for any value of $s$, so a general treatment of the fixed points and stability can be done, and second, the bulk viscosity effect is consistently handled so that it naturally turns off when matter density vanishes. As a main result we find, based on detailed dynamical system analysis, one-parameter family of de-Sitter-like asymptotic solutions with non-vanishing bulk viscosity coefficient during different cosmological periods. Numerical computations are performed jointly along with analytical phase space analysis in order to assess more quantitatively the bulk viscosity effect on the cosmological background evolution. Finally, as a first contact with observation we derive constraints on the free parameters of some bulk viscosity models with specific $s$-exponents from Supernovae Ia and observations of the Hubble parameter, by performing a Bayesian statistical analysis thought the Markov Chain Monte Carlo method.