论文标题

修改后的新兴黑能及其天文限制

Modified Emergent Dark Energy and its Astronomical Constraints

论文作者

Benaoum, H. B., Yang, Weiqiang, Pan, Supriya, Di Valentino, Eleonora

论文摘要

我们通过展示一种非常优雅的方法来介绍现象学上新兴的黑暗能量(PEDE)模型的修改形式。该模型被称为修改后的新兴暗能量(MEDE),以区分PEDE模型,其中包括$λ$ CDM,Pede模型,Chevallier-Polarski-Linder模型和其他感兴趣的宇宙学模型。我们表明,本文提供了一条非常棒的途径,以简单但非常优雅的方式构建其他Pede模型。该模型具有七个自由参数,其中六个参数与$λ$ CDM或PEDE模型相同,其余一个参数“ $α$”量化了概括。本模型预测,目前状态的暗能量方程假定为$ w _ {\ rm de} \; (z = 0)= -1- \fracα{3 \ ln(10)} $,在远的未来(即,对于$ z \ longrightarrow -1 $),它将渐近地发展至$ w _ {\ rm de} \ rm de} \ longrightRightRow -1 $。我们使用各种观察数据集对模型进行非常强大的观察性分析,包括宇宙微波背景辐射,Baryon声学振荡距离测量值,Hubble常数的局部值以及超新星类型IA的Pantheon样品。我们发现,这里的$ H_0 $张力可以缓解但无法解决,而唯一的免费参数,$α$可以恢复$λ$ CDM或PEDE的不同数据集。总而言之,本文描述了以非常简化的方式构建Pede模型的其他修改版本的新途径。

We introduce a modified form of the Phenomenologically Emergent Dark Energy (PEDE) model by showing a very elegant approach. The model is named as Modified Emergent Dark Energy (MEDE) to distinguish from PEDE model and it includes $Λ$CDM, PEDE model, Chevallier-Polarski-Linder model and other cosmological models of interest. We show that the present article offers a very fantastic route to construct other PEDE models in a simple but very elegant way. The model has seven free parameters where the six parameters are the same as in $Λ$CDM or PEDE model and the remaining one parameter `$α$' quantifies the generalization. The present model predicts that dark energy equation of state at present assumes, $w_{\rm DE}\; (z=0) = -1 - \fracα{3 \ln (10)}$ and in the far future (i.e., for $z \longrightarrow -1$), it will evolve asymptotically to $w_{\rm DE} \longrightarrow -1$. We perform a very robust observational analysis of the model using various observational datasets including cosmic microwave background radiation, baryon acoustic oscillations distance measurements, a local value of the Hubble constant and the pantheon sample of Supernovae Type Ia. We find that the $H_0$ tension here is alleviated but not solved, while the only free parameter, $α$ could recover the $Λ$CDM or PEDE for different datasets. In summary, the present article describes a novel route to construct other modified versions of the PEDE model in a very simplified manner.

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