Ia型超新星样品中异质结的探索
An Exploration of Heterogeneity in Supernova Type Ia Samples
摘要：
我们检查了三个SNe Ia型数据集：Union2.1，JLA和Panstarrs使用宇宙学盲统计分析以及宇宙学参数拟合来检查它们的一致性。我们发现，Panstarrs数据集是最稳定的三个变化，虽然它没有，目前，去足够高的红移，严格约束暗能量状态方程，$w$。Union2.1来自许多不同的来源，似乎对数据集内的变化有点敏感。JLA重建以及宇宙学参数的数量较少。在更高的自由度，它的误差对红移的依赖性可能会导致不同的子集之间的结果。Panstarrs与其他两个在大约$2σ$处不一致，JLA和Union2.1彼此相距大约$1σ$。对于$Ω_{0m}-w$宇宙学重建，对于每个数据集的选定子集，$w$中的值的$1σ$范围对于JLA和Union2.1是Panstarrs的两倍大。对于所有三个数据集，相同子集的$Ω_{0m}$中的范围保持大致相似。虽然在不同的样本中使用的拟合和校正技术存在差异，但最重要的标准是SNe选择，稍微不同的SNe选择可能会导致纯粹统计分析和宇宙学重建的明显不同的结果。我们注意到，一个单一的，高质量的低红移的样品可以帮助减少结果中的不确定性。我们还注意到，幅度误差缺乏均匀性可能会使结果产生偏差，因此应该对其进行建模，或者通过使用其他互补数据集来抵消其影响。一个在高红移和低红移都有高质量数据的超新星样本，从一些调查中构建以避免样本中的异质性，并具有均匀误差，将导致更强大的宇宙学重建。
英文摘要：
We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that Panstarrs dataset is the most stable of the three to changes, although it does not, at the moment, go to high enough redshifts to tightly constrain the dark energy equation of state, $w$. Union2.1, drawn from many different sources, appears somewhat susceptible to changes within the dataset. JLA reconstructs well for a smaller number of cosmological parameters. At higher degrees of freedom, the dependence of its errors on redshift can lead to varying results between subsets. Panstarrs is inconsistent with the other two at about $2σ$, and JLA and Union2.1 are about $1σ$ away from each other. For the $Ω_{0m}-w$ cosmological reconstruction, the $1σ$ range of values in $w$ for selected subsets of each dataset is two times larger for JLA and Union2.1 as compared to Panstarrs. The range in $Ω_{0m}$ for the same subsets remains approximately similar for all three datasets. Although there are differences in the fitting and correction techniques used in the different samples, the most important criterion is SNe selection, a slightly different SNe selection can lead to noticeably different results both in the purely statistical analysis and cosmological reconstruction. We note that a single, high quality low redshift sample could help decrease the uncertainties in the result. We also note that lack of homogeneity in the magnitude errors may bias the results and should either be modeled, or its effect neutralized by using other, complementary datasets. A supernova sample with high quality data at both high and low redshifts, constructed from a few surveys to avoid heterogeneity in the sample, and with homogeneous errors, would result in a more robust cosmological reconstruction.
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