The Gigaparsec WiggleZ simulations: Characterizing scale-dependant bias and associated systematics in growth of structure measurements

We present the Gigaparsec WiggleZ simulation suite and use this resource to characterize galaxy bias and its scale dependence for a range of redshifts and halo masses in a standard Λ cold dark matter cosmology. Under the ansatz that bias converges to a scale-independent form at large scales, we develop an eight-parameter phenomenological model which fully expresses the mass and redshift dependence of bias and its scale dependence in real- or redshift space. This is then used to illustrate how scale-dependent bias can systematically skew measurements of the growth rate of cosmic structure obtained from redshift-space distortion measurements. When data is fit only to scales kmax ≤ 0.1 [h- 1 Mpc]- 1, we find that these effects are significant only for large biases (b ≳ 3) at large redshifts (z ≳ 1). However, when smaller scales are incorporated (kmax ≲ 0.2 [h- 1 Mpc]- 1) to increase measurement precision, the combination of reduced statistical uncertainties and increased scale-dependent bias can result in highly significant systematics for most large haloes across all redshifts. We identify several new interesting aspects of bias, including a significant large-scale bias boost for small haloes at low redshifts due to substructure effects (˜20 per cent for Milky Way-like systems) and a nearly redshift-independent halo mass (corresponding to a redshift-space bias of ˜1.5) for which halo bias has little or no scale dependence on scales greater than 3 [h-1Mpc]. This suggests an optimal strategy of targeting bias ˜1.5 systems for clustering studies which are dominated more by systematic uncertainties in how observed halo (or galaxy) distributions map to their underlying mass distribution than by observational statistical precision, such as cosmological measurements of neutrino masses. Code for generating our fitting formula is publicly available at http://gbpoole.github.io/Poole_2014a_code/