The Gigaparsec WiggleZ Simulations: Characterising scale dependant bias
  and associated systematics in growth of structure measurements
  We use the Gigaparsec WiggleZ (GiggleZ) simulations to characterise galaxy
bias and its scale dependence for a range of redshifts and halo masses in a
standard $\Lambda$LCDM cosmology. Assuming bias converges to a scale
independent form at large scales, we develop a phenomenological model which
fully expresses the mass and redshift dependence of bias and its scale
dependence in real or redshift space. We then use this 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 $k_{\rm max}{\le}0.1$ $[h^{-1} \rm{Mpc}]^{-1}$, we
find that these effects are significant only for large biases ($b{\gtrsim}3$)
at large redshifts ($z{\gtrsim}1$). However, when smaller scales are
incorporated ($k_{\rm max}{\le}0.2$ $[h^{-1} \rm{Mpc}]^{-1}$) to increase
measurement precision, the combination of reduced statistical uncertainty and
increased scale dependent bias can result in highly significant systematics for
most large halos across all redshifts. We identify several new interesting
aspects of bias, including a significant large-scale bias boost for small halos
at low-redshifts due to substructure effects ($\sim$20\% for Milky Way-like
systems) and a nearly redshift-independent halo mass (corresponding to a
redshift-space bias of ${\sim}1.5$) for which halo bias has little-or-no scale
dependence on scales greater than $3$ $[h^{-1} {\rm Mpc}]$. This suggests an
optimal strategy of targeting bias ${\sim}{1.5}$ systems for clustering studies
which are dominated by systematic uncertainties in how observed halo (or
galaxy) distributions map to their underlying mass distribution, such as
cosmological measurements of neutrino masses. Code for generating our fitting
formula is publicly available at http://gbpoole.github.io/Poole_2014a_code/
(Abridged)
