EZmocks: extending the Zel'dovich approximation to generate mock galaxy catalogues with accurate clustering statistics

TL;DR

EZmocks is a new efficient method that extends the Zel'dovich approximation to generate highly accurate mock galaxy catalogues, matching full N-body simulations in clustering statistics with minimal computational resources.

Contribution

It introduces a novel approach combining Zel'dovich approximation with simple prescriptions for biasing, achieving high accuracy in clustering statistics for mock galaxy catalogues.

Findings

Achieves within 1% accuracy for power spectrum up to k=0.55 h/Mpc

Within 20% agreement for bispectrum across scales and shapes

Requires similar computational resources as log-normal models

Abstract

We present a new methodology to generate mock halo or galaxy catalogues, which have accurate clustering properties, nearly indistinguishable from full $N$-body solutions, in terms of the one-point, two-point, and three-point statistics. In particular, the agreement is remarkable, within $1\%$ up to $k=0.55$ $h$Mpc$^{-1}$ and down to $r=10$ $h^{-1}$Mpc, for the power spectrum and two-point correlation function respectively, while the bispectrum agrees in general within $20\%$ for different scales and shapes. Our approach is based on the Zel'dovich approximation, however, effectively including with the simple prescriptions the missing physical ingredients, and stochastic scale-dependent, non-local and nonlinear biasing contributions. The computing time and memory required to produce one mock is similar to that using the log-normal model. With high accuracy and efficiency, the effective Zel'dovich approximation mocks (EZmocks) provide a reliable and practical method to produce massive mock galaxy catalogues for the analysis of large-scale structure measurements.