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arXiv 1206.4948astro-ph.CO
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用引力透镜统计检验暗能量

Testing the dark energy with gravitational lensing statistics

Shuo Cao, Giovanni Covone, Zong-Hong Zhu

摘要:

我们研究了从122个系统中提取的两个早期引力透镜样品的红移分布,以约束LCDM模型中的宇宙学常数和一组替代暗能量模型(XCDM,Dvali-Gabadadze-Porrati和Ricci暗能量模型)的参数,在空间平坦的宇宙中。当考虑不包括SLACS系统(已知偏向大图像分离透镜)和无演化的样本时,$Ω_Λ= 0.70 \pm 0.09$的可能性最大,当限制图像分离大于2”和无演化的重力透镜时,$Ω_Λ= 0.81\pm 0.05$的可能性最大。在这两种情况下,解释星系演化的结果在1 $σ$内是一致的。本测试通过排除零假设(即,$Ω_Λ= 0 $)在4个以上的$σ$,无论选择的样本和假设的星系演化。通过贝叶斯信息准则对竞争世界模型进行了比较。这表明最简单的宇宙学常数模型--只有一个自由参数--仍然是引力透镜红移分布的可用数据所偏爱的。我们进行了可能的系统效应的分析,发现由于样本不完整性,星系演化和模型的不确定性的系统误差近似等于统计误差,与现今的数据。我们发现,系统误差的最大来源是动态归一化和高速截止因子,其次是速度弥散函数的弱端斜率。

英文摘要:

We study the redshift distribution of two samples of early-type gravitational lenses, extracted from a larger collection of 122 systems, to constrain the cosmological constant in the LCDM model and the parameters of a set of alternative dark energy models (XCDM, Dvali-Gabadadze-Porrati and Ricci dark energy models), under a spatially flat universe. The likelihood is maximized for $Ω_Λ= 0.70 \pm 0.09$ when considering the sample excluding the SLACS systems (known to be biased towards large image-separation lenses) and no-evolution, and $Ω_Λ= 0.81\pm 0.05$ when limiting to gravitational lenses with image separation larger than 2" and no-evolution. In both cases, results accounting for galaxy evolution are consistent within 1$σ$. The present test supports the accelerated expansion, by excluding the null-hypothesis (i.e., $Ω_Λ= 0 $) at more than 4$σ$, regardless of the chosen sample and assumptions on the galaxy evolution. A comparison between competitive world models is performed by means of the Bayesian information criterion. This shows that the simplest cosmological constant model - that has only one free parameter - is still preferred by the available data on the redshift distribution of gravitational lenses. We perform an analysis of the possible systematic effects, finding that the systematic errors due to sample incompleteness, galaxy evolution and model uncertainties approximately equal the statistical errors, with present-day data. We find that the largest sources of systemic errors are the dynamical normalization and the high-velocity cut-off factor, followed by the faint-end slope of the velocity dispersion function.

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