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Constraining the dark energy models with H(z) data: An approach independent of H0

机译:用H(z)数据约束暗能量模型:一种独立于H0的方法

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We study the performance of the latest H(z) data in constraining the cosmological parameters of different cosmological models, including that of Chevalier-Polarski-Linder w0w1 parametrization. First, we introduce a statistical procedure in which the chi-square estimator is not affected by the value of the Hubble constant. As a result, we find that the H(z) data do not rule out the possibility of either nonflat models or dynamical dark energy cosmological models. However, we verify that the time varying equation-of-state parameter w(z) is not constrained by the current expansion data. Combining the H(z) and the Type Ia supernova data, we find that the H(z)/SNIa overall statistical analysis provides a substantial improvement of the cosmological constraints with respect to those of the H(z) analysis. Moreover, the w_0 ? w_1 parameter space provided by the H(z)/SNIa joint analysis is in very good agreement with that of Planck 2015, which confirms that the present analysis with the H(z) and supernova type Ia (SNIa) probes correctly reveals the expansion of the Universe as found by the team of Planck. Finally, we generate sets of Monte Carlo realizations in order to quantify the ability of the H(z) data to provide strong constraints on the dark energy model parameters. The Monte Carlo approach shows significant improvement of the constraints, when increasing the sample to 100 H(z) measurements. Such a goal can be achieved in the future, especially in the light of the next generation of surveys.
机译:本文研究了最新H(z)数据在约束不同宇宙学模型的宇宙学参数方面的性能,包括Chevalier-Polarski-Linder w0w1参数化。首先,我们引入了一个统计程序,其中卡方估计器不受哈勃常数值的影响。因此,我们发现H(z)数据并不排除非平坦模型或动态暗能量宇宙学模型的可能性。然而,我们验证了时变状态方程参数 w(z) 不受当前展开数据的约束。结合H(z)和Ia型超新星数据,我们发现H(z)/SNIa总体统计分析相对于H(z)分析的宇宙学约束有实质性的改善。此外,w_0?H(z)/SNIa联合分析提供的w_1参数空间与普朗克2015的参数空间非常吻合,这证实了目前使用H(z)和超新星Ia型(SNIa)探测器的分析正确地揭示了普朗克团队发现的宇宙膨胀。最后,我们生成了蒙特卡罗实现集,以量化H(z)数据对暗能量模型参数提供强约束的能力。当将样品增加到 100 H(z) 测量值时,蒙特卡罗方法显示出约束条件的显着改善。这样的目标在未来是可以实现的,特别是考虑到下一代的调查。

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