Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: Dynamical dark energy

Abstract

We perform an anisotropic clustering analysis of 1,133,326 galaxies from the Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS) Data Release (DR) 12 covering the redshift range $0.15<z<0.69$. The geometrical distortions of the galaxy positions, caused by incorrect cosmological model assumptions, are captured in the anisotropic two-point correlation function on scales 6 -- 40 $h^{-1}\mathrm{Mpc}$. The redshift evolution of this anisotropic clustering is used to place constraints on the cosmological parameters. We improve the methodology of Li et al. 2016, to enable efficient exploration of high dimensional cosmological parameter spaces, and apply it to the Chevallier-Polarski-Linder parametrization of dark energy, $w=w_0+w_{a}z/(1+z)$. In combination with the CMB, BAO, SNIa and $H_0$ from Cepheid data, we obtain ${\Omega }_m=0.301\pm 0.008,w_0=-1.042\pm 0.067,$ and $w_a=-0.07\pm 0.29$ (68.3\% CL). Adding our new AP measurements to the aforementioned results reduces the error bars by $\sim$30 -- 40\% and improves the dark energy figure of merit by a factor of $\sim$2. We check the robustness of the results using realistic mock galaxy catalogues.

12 pages, 9 figures, accepted to ApJ

References in corpus (7)

• The Clustering of the SDSS DR7 Main Galaxy Sample I: A 4 per cent Distance Measure at z=0.15
• Beyond the Cosmological Standard Model
• Why your model parameter confidences might be too optimistic -- unbiased estimation of the inverse covariance matrix
• Theoretical Models of Dark Energy
• Cosmological constraints from the redshift dependence of the Alcock-Paczynski effect: application to the SDSS-III BOSS DR12 galaxies
• Cosmological constraints from the redshift dependence of the Alcock-Paczynski test and volume effect: galaxy two-point correlation function
• Using galaxy pairs as cosmological tracers

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