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arXiv 1809.05094astro-ph.COastro-ph.HEhep-ph
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联合光变曲线分析的宇宙学检验

Cosmological Tests with the Joint Lightcurve Analysis

Fulvio Melia, Jun-Jie Wei, Robert Maier, Xuefeng Wu

摘要:

我们检查是否wCDM和R_h=ct之间的比较使用合并Ia型SN目录产生的结果与基于一个单一的均匀样品的结果一致。使用Betoule等人(2014)对SNLS和SDSS-II的613个事件的组合样本进行联合光曲线分析(JLA),我们估计了两个模型的参数并进行了比较。我们发现,改进的统计可以改变模型选择在某些情况下,但不是其他人。此外,根据模型拟合,我们发现,似乎有一个挥之不去的系统之间的SNLS和SDSS-II源的偏移约0.04-0.08等,尽管在JLA的交叉校准。将wCDM、LCDM和R_h=ct作为单独的模型处理,我们在无偏的成对统计比较中发现,贝叶斯信息准则(BIC)有利于R_h=ct论域,其似然率为82.8%对wCDM的17.2%,但当w_de=-1时,似然率的比率是相反的(16.2%对83.8%)(即,LCDM),并且如果另外k=0(即,扁平LCDM)。然而,我们指出,k的值是宇宙中净能量(动能加引力)的度量,理论上不受约束,尽管由于膨胀强制稀释,某些暴胀模型会将k推至0。由于我们在这里只考虑基本的LCDM模型,k的值需要测量,因此,平坦性的预先假设将显著的偏差引入BIC。

英文摘要:

We examine whether a comparison between wCDM and R_h=ct using merged Type Ia SN catalogs produces results consistent with those based on a single homogeneous sample. Using the Betoule et al. (2014) joint lightcurve analysis (JLA) of a combined sample of 613 events from SNLS and SDSS-II, we estimate the parameters of the two models and compare them. We find that the improved statistics can alter the model selection in some cases, but not others. In addition, based on the model fits, we find that there appears to be a lingering systematic offset of ~0.04-0.08 mag between the SNLS and SDSS-II sources, in spite of the cross-calibration in the JLA. Treating wCDM, LCDM and R_h=ct as separate models, we find in an unbiased pairwise statistical comparison that the Bayes Information Criterion (BIC) favors the R_h=ct universe with a likelihood of 82.8% versus 17.2% for wCDM, but the ratio of likelihoods is reversed (16.2% versus 83.8%) when w_de=-1 (i.e., LCDM) and strongly reversed (1.0% versus 99.0%) if in addition k=0 (i.e., flat LCDM). We point out, however, that the value of k is a measure of the net energy (kinetic plus gravitational) in the Universe and is not constrained theoretically, though some models of inflation would drive k to 0 due to an expansion-enforced dilution. Since we here consider only the basic LCDM model, the value of k needs to be measured and, therefore, the pre-assumption of flatness introduces a significant bias into the BIC.

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