品质因数和观察数据集的不同组合
Figure of Merit and Different Combinations of Observational Data Sets
摘要：
为了约束宇宙学参数，人们经常对观测数据集的不同组合进行联合分析。本文利用暗能量任务组基准模型（CPL模型）的优值（FoM）对11个广泛使用的观测数据集（Ia型超新星、观测哈勃参数、重子声学振荡、宇宙微波背景、X射线团重子质量分数和伽马射线暴）进行了优值估计。本文从文献中常用的两类基本组合出发，分析了不同的组合，并对两类组合进行了比较。我们发现了两组对暗能量参数有较强约束能力的组合，一组具有最大的FoM，另一组包含较少的观测数据，但FoM相对较大，拟合过程简单。
英文摘要：
To constrain cosmological parameters, one often makes a joint analysis with different combinations of observational data sets. In this paper we take the figure of merit (FoM) for Dark Energy Task Force fiducial model (CPL model) to estimate goodness of different combinations of data sets, which include 11 widely-used observational data sets (Type Ia Supernovae, Observational Hubble Parameter, Baryon Acoustic Oscillation, Cosmic Microwave Background, X-ray Cluster Baryon Mass Fraction, and Gamma-Ray Bursts). We analyze different combinations and make a comparison for two types of combination based on two types of basic combinations, which are often adopted in the literatures. We find two sets of combinations, which have strong ability to constrain the dark energy parameters, one has the largest FoM, the other contains less observational data with a relative large FoM and a simple fitting procedure.
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