Francisco-Shu Kitaura, Gustavo Yepes, Francisco Prada
In this work we investigate the generation of mock halo catalogs based on perturbation theory and nonlinear stochastic biasing with the novel PATCHY-code. In particular, we use Augmented Lagrangian Perturbation Theory (ALPT) to generate a dark matter density field on a mesh starting from Gaussian fluctuations. ALPT is based on a combination of second order LPT (2LPT) on large scales and the spherical collapse model on smaller scales. We account for the systematic deviation of perturbative approaches from N-body simulations together with halo biasing adopting an exponential bias. We then account for stochastic biasing by defining three regimes: a low, an intermediate and a high density regime, using a Poisson distribution in the intermediate regime and the negative binomial distribution including an additional parameter to model over-dispersion in the high density regime. Since we focus in this study on massive halos, we suppress the generation of halos in the low density regime. The various nonlinear biasing parameters, stochastic biasing parameter and density thresholds are calibrated with the large BigMultiDark N-body simulation to match the power spectrum of the corresponding halo population. Our model effectively includes only 4 parameters, as they are additionally constrained by the number density. Our mock catalogs show power spectra which are compatible with N-body simulations within about 2% up to k ~ 1 h Mpc^-1 at redshift z = 0.577 for a sample of halos with the typical BOSS CMASS galaxy number density. The corresponding correlation functions are compatible down to a few Mpc. We also find that neglecting over-dispersion in high density regions produces power spectra with deviations of 10% at k ~ 0.4 h Mpc^-1. These results indicate the need to account for an accurate statistical description of the galaxy clustering for precise studies of large-scale surveys
View original:
http://arxiv.org/abs/1307.3285
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