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

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 per cent up to k = 0.55 hMpc(-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 per cent 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 non-linear biasing contributions. The computing time and memory required to produce one mock is similar to that using the lognormal 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.