论文标题

用密度参数化重建宇宙进化

Reconstructing cosmic evolution with a density parametrization

论文作者

Nagpal, Ritika, Pacif, Shibesh Kumar Jas, Parida, Abhishek

论文摘要

当前的论文在经典制度中对深色能源宇宙学模型进行了全面的检查,其中通用标量场被视为暗能量源。爱因斯坦的场方程以独立模型方式求解,即使用宇宙学参数化方案。在这一行中研究了密度参数作为宇宙量表因子的函数的参数化。结果值得注意,因为它显示出最近从减速到加速阶段的平稳过渡。此处使用的参数化方法功能形式所涉及的模型参数使用某些外部数据集约束。此处使用的更新的Hubble数据集,其中包含57个数据点,最近编译的Pantheon数据集的1048点以及此处使用Baryon声学振荡(BAO)数据集来确定最佳拟合模型参数值。几个重要的宇宙学参数的表达方式表示为红移`$ z $'的函数,并以视觉说明了模型参数的最佳拟合值,以更好地理解宇宙演化。还将获得的模型与$λCDM$模型进行了比较。我们的模型在将来具有独特的行为,并显示出很大的紧缩类型崩溃。模型参数的最佳拟合值还用于计算几种物理和几何参数的电流值以及相变红移。为了检查暗能量的性质,在衍生模型上进行了某些宇宙学测试和诊断分析。

The current paper provides a comprehensive examination of a dark energy cosmological model in the classical regime, in which a generic scalar field is regarded as a dark energy source. Einstein's field equations are solved in model independent way i.e. using a scheme of cosmological parametrization. A parametrization of the density parameter as a function of the cosmic scale factor has been investigated in this line. The result is noteworthy because it shows a smooth transition from a decelerating to an accelerating phase in the recent past. The model parameters involved in the functional form of the parametrization approach utilized here were constrained using certain external datasets. The updated Hubble datasets containing 57 datapoints, 1048 points of recently compiled Pantheon datasets, and also the Baryon Acoustic Oscillation (BAO) datasets are used here to determine the best-fitting model parameter values. The expressions of several significant cosmological parameters are represented as a function of redshift `$z$' and illustrated visually for the best fit values of the model parameters to better comprehend cosmic evolution. The obtained model is also compared with the $ΛCDM$ model. Our model has a distinct behavior in future and shown a big crunch type collapse. The best fit values of the model parameters are also used to compute the current values of several physical and geometrical parameters, as well as phase transition redshift. To examine the nature of dark energy, certain cosmological tests and diagnostic analyses are done on the derived model.

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