Modelling Baryon Acoustic Oscillations with Perturbation Theory and Stochastic Halo Biasing
Francisco-Shu Kitaura, Gustavo Yepes, Francisco Prada

TL;DR
This paper introduces a novel method using perturbation theory and stochastic biasing to generate accurate mock halo catalogues, matching N-body simulations within 2% for power spectra and improving galaxy clustering modeling.
Contribution
The work develops the PATCHY-code combining ALPT and stochastic biasing, calibrated with N-body simulations, to produce realistic halo catalogues for large-scale structure studies.
Findings
Mock catalogues match N-body power spectra within 2% up to k ~ 1 h Mpc^-1.
Neglecting over-dispersion causes 10% deviations at k ~ 0.4 h Mpc^-1.
The method accurately reproduces real- and redshift-space clustering statistics.
Abstract
In this work we investigate the generation of mock halo catalogues 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 and to compute the peculiar velocity field. 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 model. 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 to model over-dispersion in the high density…
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