HALOGEN: A tool for fast generation of mock halo catalogues
  We present a simple method of generating approximate synthetic halo
catalogues: HALOGEN. This method uses a combination of $2^{nd}$-order
Lagrangian Perturbation Theory (2LPT) in order to generate the large-scale
matter distribution, analytical mass functions to generate halo masses, and a
single-parameter stochastic model for halo bias to position haloes. HALOGEN
represents a simplification of similar recently published methods.
  Our method is constrained to recover the 2-point function at intermediate
($10Mpc/h<r<50Mpc/h$) scales, which we show is successful to within 2 per cent.
Larger scales ($\sim100Mpc/h$) are reproduced to within 15 per cent. We compare
several other statistics (e.g. power spectrum, point distribution function,
redshift space distortions) with results from N-Body simulations to determine
the validity of our method for different purposes. One of the benefits of
HALOGEN is its flexibility, and we demonstrate this by showing how it can be
adapted to varying cosmologies and simulation specifications.
  A driving motivation for the development of such approximate schemes is the
need to compute covariance matrices and study the systematic errors for large
galaxy surveys, which requires thousands of simulated realisations. We discuss
the applicability of our method in this context, and conclude that it is well
suited to mass production of appropriate halo catalogues.
  The code is publicly available at https://github.com/savila/halogen
