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 $1\%$ 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.
