Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2012-02-24
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
16 pages, 14 figures, submitted to MNRAS
Scientific paper
We investigate the ability of state-of-the-art redshift-space distortions models for the galaxy anisotropic two-point correlation function \xi(r_p, r_\pi), to recover precise and unbiased estimates of the linear growth rate of structure f, when applied to catalogues of galaxies characterised by a realistic bias relation. To this aim, we make use of a set of simulated catalogues at z=0.1 and z=1 with different luminosity thresholds, obtained by populating dark-matter haloes from a large N-body simulation using halo occupation prescriptions. We examine the most recent developments in redshift-space distortions modelling, which account for non-linearities on both small and intermediate scales produced respectively by randomised motions in virialised structures and non-linear coupling between the density and velocity fields. We consider the possibility of including the linear component of galaxy bias as a free parameter and directly estimate the growth rate of structure f. Results are compared to those obtained using the standard dispersion model, over different ranges of scales.We find that the model of Taruya et al. (2010), the most sophisticated one considered in this analysis, provides in general the most unbiased estimates of the growth rate of structure, with systematic errors within 4% over a wide range of galaxy populations spanning luminosities between L > L^* and L > 3L^*. Accounting for the scale-dependence of galaxy bias plays a crucial role in recovering an unbiased estimate of f when fitting quasi non-linear scales. Its impact is found to be more severe for highly-biased tracers such as Luminous Red Galaxies, for which systematic effects in the modelling might be more difficult to mitigate and have to be further investigated. [...]
Guzzo Luigi
la Torre Sylvain de
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