http://arxiv.org/abs/1407.2637
(Abridged) We present estimates of the nonlinear bias of cosmological haloes spanning a wide range in mass, from $\sim 10^{5} M_\odot$ to $\sim 10^{12} M_\odot$, by combining the empirical, average mass function derived from a suite of high-resolution cosmological N-body simulations, and the theoretical nonlinear bias parameter based on the extended Press-Schechter formalism. The halo bias is expressed in terms of the mean bias and stochasticity as a function of local overdensity ($\delta$), based on different filtering scales, which is realized as the density of individual cells in uniform grids. The sampled overdensities span a range large enough to include both linear and nonlinear regimes, allowing us to obtain the fully nonlinear bias effect on the formation of haloes. A very strong correlation between $\delta$ and halo population overdensity $\delta_h$, or nonlinear bias, is found, along with sizable stochasticity. We find that the empirical mean halo bias matches, with good accuracy, the prediction by the peak-background split method based on the excursion set formalism, as long as the empirical, globally-averaged halo mass function is used. Consequently, this bias formalism is insensitive to uncertainties caused by varying halo identification schemes, and can be applied generically. We also find that the probability distribution function of biased halo numbers has wider distribution than the pure Poisson shot noise, which is attributed to the sub-cell scale halo correlation. We explicitly calculate this correlation function and show that both overdense and underdense regions have positive correlation, leading to stochasticity larger than the Poisson shot noise in the range of haloes and halo-collapse epochs we study. Our results can be used to generate mock halo catalogues once a density field is given, such as in cosmological N-body simulations of structure formation. (Abridged)
K. Ahn, I. Iliev, P. Shapiro, et. al.
Fri, 11 Jul 14
7/49
Comments: 36 pages, 22 figures, MNRAS submitted
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