Modeling scale-dependent bias on the baryonic acoustic scale with the statistics of peaks of Gaussian random fields
Vincent Desjacques, Martin Crocce, Roman Scoccimarro, Ravi K. Sheth

TL;DR
This paper extends the peak model of Gaussian random fields to better predict scale-dependent bias in galaxy clustering, especially around the BAO scale, by deriving higher-order correlations and including gravitational evolution effects.
Contribution
The paper introduces second-order peak correlation functions and incorporates gravitational evolution to improve bias predictions on BAO scales.
Findings
Peak model accurately predicts ~5% residual bias at BAO scale.
Scale-dependent bias observed in large-scale halo clustering.
Model reproduces simulation results for massive halo bias.
Abstract
Models of galaxy and halo clustering commonly assume that the tracers can be treated as a continuous field locally biased with respect to the underlying mass distribution. In the peak model pioneered by BBKS, one considers instead density maxima of the initial, Gaussian mass density field as an approximation to the formation site of virialized objects. In this paper, the peak model is extended in two ways to improve its predictive accuracy. Firstly, we derive the two-point correlation function of initial density peaks up to second order and demonstrate that a peak-background split approach can be applied to obtain the k-independent and k-dependent peak bias factors at all orders. Secondly, we explore the gravitational evolution of the peak correlation function within the Zel'dovich approximation. We show that the local (Lagrangian) bias approach emerges as a special case of the peak…
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