Redshift space correlations and scale-dependent stochastic biasing of density peaks

Details Redshift space correlations and scale-dependent stochastic biasing of density peaks Redshift space correlations and scale-dependent stochastic biasing of density peaks

2009-09-24

arxiv.org/abs/0909.4544v4

Phys.Rev.D81:023526,2010

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

24 pages, 7 figures (v3): references added (v4): added figure+appendix. In press in PRD

Scientific paper

10.1103/PhysRevD.81.023526

We calculate the redshift space correlation function and the power spectrum of density peaks of a Gaussian random field. In the linear regime k < 0.1 h/Mpc, the redshift space power spectrum is P^s_{pk}(k,u) = exp(-f^2 s_{vel}^2 k^2 u^2) * [b_{pk}(k) + b_{vel}(k) f u^2]^2 * P_m(k), where u is the angle with respect to the line of sight, s_{vel} is the one-dimensional velocity dispersion, f is the growth rate, and b_{pk}(k) and b_{vel}(k) are k-dependent linear spatial and velocity bias factors. For peaks, the value of s_{vel} depends upon the functional form of b_{vel}. The peaks model is remarkable because it has unbiased velocities -- peak motions are driven by dark matter flows -- but, in order to achieve this, b_{vel} is k-dependent. We speculate that this is true in general: k-dependence of the spatial bias will lead to k-dependence of b_{vel} even if the biased tracers flow with the dark matter. Because of the k-dependence of the linear bias parameters, standard manipulations applied to the peak model will lead to k-dependent estimates of the growth factor that could erroneously be interpreted as a signature of modified dark energy or gravity. We use the Fisher formalism to show that the constraint on the growth rate f is degraded by a factor of two if one allows for a k-dependent velocity bias of the peak type. We discuss a simple estimate of nonlinear evolution and illustrate the effect of the peak bias on the redshift space multipoles. For k < 0.1 h/Mpc, the peak bias is deterministic but k-dependent, so the configuration space bias is stochastic and scale dependent, both in real and redshift space. We provide expressions for this stochasticity and its evolution (abridged).

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