论文标题

高红移宇宙学:应用和与不同方法的比较

High-redshift cosmography: Application and comparison with different methods

论文作者

Hu, J. P., Wang, F. Y.

论文摘要

宇宙学用于宇宙学数据处理,以便以模型无关的方式限制宇宙的运动学。在本文中,我们首先研究了紫外线(UV)和X射线关系对宇宙学约束的影响。通过同时拟合类星体关系和cosmographic参数,我们发现4 $σ$偏离宇宙常数冷暗物质($λ$ CDM)模型消失了。接下来,利用Pantheon样品和31个长伽马射线爆发(LGRB),我们在不同的cosmogrogron扩展($ z $ -REDSHIFT,$ y $ -REDSHIFT,$ e(y)$,$ e(y)$,$ \ log(1+z)$,$ y $ e(1+z)$,$ \ log(1+z)+k_ {ij {ij {ij} $中三阶和四阶扩展。扩展顺序可能会严重影响结果,尤其是对于$ y $ redshift方法。通过来自同一样本的分析,除了$ y $ -REDSHIFT和$ e(y)$方法外,低阶扩展是可取的。对于$ y $ redshift和$ e(y)$方法,尽管采用了相同的参数化$ y = z/(1+z)$,但后者的性能优于前者。对数多项式,$ \ log(1+z)$和$ \ log(1+z)+k_ {ij} $,其性能明显优于$ z $ -REDSHIFT,$ y $ -REDSHIFT和$ e(y)$,但比PAD $ \ pAD $ \ rm \ rm \ rm \ rm \ rm \ rm \ rm \ rm \ rm \ rm \ rm \ atm \ atmute {e {e} $近似。最后,我们全面分析了从不同样本获得的结果。我们发现PAD $ \ rm \ acute {e} _ {(2,1)} $方法适用于低红移和高红移案例。 PAD $ \ rm \急性{e} _ {(2,2)} $方法在高红移的情况下表现良好。对于$ y $ redshift和$ e(y)$方法,对前两个参数($ q_ {0} $和$ j_ {0} $)的唯一约束是可靠的。

Cosmography is used in cosmological data processing in order to constrain the kinematics of the universe in a model-independent way. In this paper, we first investigate the effect of the ultraviolet (UV) and X-ray relation of a quasar on cosmological constraints. By fitting the quasar relation and cosmographic parameters simultaneously, we find that the 4$σ$ deviation from the cosmological constant cold dark matter ($Λ$CDM) model disappears. Next, utilizing the Pantheon sample and 31 long gamma-ray bursts (LGRBs), we make a comparison among the different cosmographic expansions ($z$-redshift, $y$-redshift, $E(y)$, $\log(1+z)$, $\log(1+z)+k_{ij}$, and Pad$\rm \acute{e}$ approximations) with the third-order and fourth-order expansions. The expansion order can significantly affect the results, especially for the $y$-redshift method. Through analysis from the same sample, the lower-order expansion is preferable, except the $y$-redshift and $E(y)$ methods. For the $y$-redshift and $E(y)$ methods, despite adopting the same parameterization of $y=z/(1+z)$, the performance of the latter is better than that of the former. Logarithmic polynomials, $\log(1+z)$ and $\log(1+z) + k_{ij}$, perform significantly better than $z$-redshift, $y$-redshift, and $E(y)$ methods, but worse than Pad$\rm \acute{e}$ approximations. Finally, we comprehensively analyze the results obtained from different samples. We find that the Pad$\rm \acute{e}_{(2,1)}$ method is suitable for both low and high redshift cases. The Pad$\rm \acute{e}_{(2,2)}$ method performs well in a high-redshift situation. For the $y$-redshift and $E(y)$ methods, the only constraint on the first two parameters ($q_{0}$ and $j_{0}$) is reliable.

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