论文标题
某些参数化的深色能量模型的最新数据约束
Latest data constraint of some parameterized dark energy models
论文作者
论文摘要
我们使用各种最新的宇宙学数据集，包括类型的超新星，宇宙微波背景辐射，Baryon声学振荡以及哈勃参数的估计，我们测试了具有状态参数化方程的某些暗能量模型，并尝试区分或选择观察观察者。我们获得了六个模型的最佳拟合结果，并计算其Akaike信息标准和贝叶斯信息标准的值。我们可以通过使用这两个信息标准将这些暗能量模型彼此区分。但是，$λ$ CDM型号仍然是最佳拟合模型。此外，我们执行包括状态诊断和OM诊断在内的几何诊断，以了解暗能量模型的几何行为。我们发现，可以将六个DE模型彼此区分开，并从$λ$ CDM，Chaplygin气体，在状态基础和OM诊断之后进行典型模型。最后，我们将黑暗能源模型的生长因子与$λ$ CDM模型进行了比较。尽管如此，我们发现这些模型可以彼此区分，并通过增长因子近似与$λ$ CDM模型区分开。
Using various latest cosmological datasets including Type-Ia supernovae, cosmic microwave background radiation, baryon acoustic oscillations, and estimations of the Hubble parameter, we test some dark energy models with parameterized equations of state and try to distinguish or select observation-preferred models. We obtain the best fitting results of the six models and calculate their values of the Akaike Information Criteria and Bayes Information Criterion. And we can distinguish these dark energy models from each other by using these two information criterions. However, the $Λ$CDM model remains the best fit model. Furthermore, we perform geometric diagnostics including statefinder and Om diagnostics to understand the geometric behaviour of the dark energy models. We find that the six DE models can be distinguished from each other and from $Λ$CDM, Chaplygin gas, quintessence models after the statefinder and Om diagnostics were performed. Finally, we consider the growth factor of the dark energy models with comparison to $Λ$CDM model. Still, we find the models can be distinguished from each other and from $Λ$CDM model through the growth factor approximation.
