Non-linear Stochastic Galaxy Biasing in Cosmological Simulations

We study the biasing relation between dark-matter halos or galaxies and the underlying mass distribution, using cosmological $N$-body simulations in which galaxies are modelled via semi-analytic recipes. The nonlinear, stochastic biasing is quantified in terms of the mean biasing function and the scatter about it as a function of time, scale and object properties. The biasing of galaxies and halos shows a general similarity and a characteristic shape, with no galaxies in deep voids and a steep slope in moderately underdense regions. At $\sim 8\hmpc$, the nonlinearity is typically $\lsim 10$ percent and the stochasticity is a few tens of percent, corresponding to $\sim 30$ percent variations in the cosmological parameter $\beta=\Omega^{0.6}/b$. Biasing depends weakly on halo mass, galaxy luminosity, and scale. The time evolution is rapid, with the mean biasing larger by a factor of a few at $z\sim 3$ compared to $z=0$, and with a minimum for the nonlinearity and stochasticity at an intermediate redshift. Biasing today is a weak function of the cosmological model, reflecting the weak dependence on the power-spectrum shape, but the time evolution is more cosmology-dependent, relecting the effect of the growth rate. We provide predictions for the relative biasing of galaxies of different type and color, to be compared with upcoming large redshift surveys. Analytic models in which the number of objects is conserved underestimate the evolution of biasing, while models that explicitly account for merging provide a good description of the biasing of halos and its evolution, suggesting that merging is a crucial element in the evolution of biasing.