arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1006.0296astro-ph.CO
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利用SN,BAO,OHD,fgas数据集对Ricci暗能量模型的观测约束

Observational Constraints to Ricci Dark Energy Model by Using: SN, BAO, OHD, fgas Data Sets

Lixin Xu, Yuting Wang

摘要:

本文采用马尔可夫链蒙特卡罗(MCMC)方法,结合星系团X射线气体质量分数、Ia(397)型超新星、重子声振荡、当前宇宙微波背景和观测哈勃函数的观测数据,对Ricci暗能量模型进行了整体约束.在平面模型中,我们得到了$1σ, 2σ$区域中参数的最佳拟合值:$Ω_{m0}=0.2927^{+0.0420 +0.0542}_{-0.0323 -0.0388}$,$α=0.3823^{+0.0331 +0.0415}_{-0.0418 -0.0541}$,$Age/Gyr=13.48^{+0.13 +0.17}_{-0.16 -0.21}$,$H_0=69.09^{+2.56 +3.09}_{-2.37 -3.39}$。在非平坦模型中,最佳拟合参数位于$1σ, 2σ$区域:$Ω_{m0}=0.3003^{+0.0367 +0.0429}_{-0.0371 -0.0423}$、$α=0.3845^{+0.0386 +0.0521}_{-0.0474 -0.0523}$、$Ω_k=0.0240^{+0.0109 +0.0133}_{-0.0130 -0.0153}$、$Age/Gyr=12.54^{+0.51 +0.65}_{-0.37 -0.49}$、$H_0=72.89^{+3.31 +3.88}_{-3.05 -3.72}$。通过与使用相同数据集的$Λ\textmd{CDM}$模型的约束结果进行比较,表明当前组合数据集更倾向于$Λ\textmd{CDM}$模型而不是Ricci暗能量模型.

英文摘要:

In this paper, we perform a global constraint on the Ricci dark energy model with both the flat case and the non-flat case, using the Markov Chain Monte Carlo (MCMC) method and the combined observational data from the cluster X-ray gas mass fraction, Supernovae of type Ia (397), baryon acoustic oscillations, current Cosmic Microwave Background, and the observational Hubble function. In the flat model, we obtain the best fit values of the parameters in $1σ, 2σ$ regions: $Ω_{m0}=0.2927^{+0.0420 +0.0542}_{-0.0323 -0.0388}$, $α=0.3823^{+0.0331 +0.0415}_{-0.0418 -0.0541}$, $Age/Gyr=13.48^{+0.13 +0.17}_{-0.16 -0.21}$, $H_0=69.09^{+2.56 +3.09}_{-2.37 -3.39}$. In the non-flat model, the best fit parameters are found in $1σ, 2σ$ regions:$Ω_{m0}=0.3003^{+0.0367 +0.0429}_{-0.0371 -0.0423}$, $α=0.3845^{+0.0386 +0.0521}_{-0.0474 -0.0523}$, $Ω_k=0.0240^{+0.0109 +0.0133}_{-0.0130 -0.0153}$, $Age/Gyr=12.54^{+0.51 +0.65}_{-0.37 -0.49}$, $H_0=72.89^{+3.31 +3.88}_{-3.05 -3.72}$. Compared to the constraint results in the $Λ\textmd{CDM}$ model by using the same datasets, it is shown that the current combined datasets prefer the $Λ\textmd{CDM}$ model to the Ricci dark energy model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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