用红移分组参数化方法拟合组成SNIa数据
Fitting the Constitution SNIa Data with Redshift Binned Parameterization Method
摘要：
在这项工作中，我们探讨了最近公布的397个Ia型超新星（SNIa）的宪法样本的宇宙学后果。通过重新考察Chevallier-Polarski-Linder（CPL）参数化，我们发现，单独拟合本构集时，暗能量的行为明显偏离宇宙学常数$Λ$，状态方程$w$和暗能量密度$ρ_Λ$将随着红移$z$的增加而迅速减小.受此启发，我们将红移分成不同的仓，并分别讨论了每个仓中的常数$w$和常数$ρ_Λ$的模型。研究发现，在单独拟合本构模型时，随着$z$的增加，$w$和$ρ_Λ$也会迅速减小，这与CPL模型的结果一致。此外，阶跃函数模型中，$ρ_Λ$迅速减少红移$z\sim0.331$提出了一个显着的改善（$Δχ^{2}=-4.361$）CPL参数化，并表现得比其他DE模型。我们还绘制了这个模型的DE密度的误差条，并发现这个模型偏离宇宙学常数$Λ$在68.3%的置信水平（CL），这可能是由于一些偏置系统误差在处理SNIa数据，或更有趣的是从DE本身的性质。此外，对于具有相同数量的红移箱的模型，分段常数$ρ_Λ$模型总是比分段常数$w$模型表现得更好，这显示了使用$ρ_Λ$而不是$w$来探测DE的变化的优势。
英文摘要：
In this work, we explore the cosmological consequences of the recently released Constitution sample of 397 Type Ia supernovae (SNIa). By revisiting the Chevallier-Polarski-Linder (CPL) parameterization, we find that, for fitting the Constitution set alone, the behavior of dark energy (DE) significantly deviate from the cosmological constant $Λ$, where the equation of state (EOS) $w$ and the energy density $ρ_Λ$ of DE will rapidly decrease along with the increase of redshift $z$. Inspired by this clue, we separate the redshifts into different bins, and discuss the models of a constant $w$ or a constant $ρ_Λ$ in each bin, respectively. It is found that for fitting the Constitution set alone, $w$ and $ρ_Λ$ will also rapidly decrease along with the increase of $z$, which is consistent with the result of CPL model. Moreover, a step function model in which $ρ_Λ$ rapidly decreases at redshift $z\sim0.331$ presents a significant improvement ($Δχ^{2}=-4.361$) over the CPL parameterization, and performs better than other DE models. We also plot the error bars of DE density of this model, and find that this model deviates from the cosmological constant $Λ$ at 68.3% confidence level (CL); this may arise from some biasing systematic errors in the handling of SNIa data, or more interestingly from the nature of DE itself. In addition, for models with same number of redshift bins, a piecewise constant $ρ_Λ$ model always performs better than a piecewise constant $w$ model; this shows the advantage of using $ρ_Λ$, instead of $w$, to probe the variation of DE.
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。
