HALOGEN: A tool for fast generation of mock halo catalogues

Abstract

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 ($\sim 100Mpc/h$) 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

Submitted to MNRAS, 14 pages, 12 figures

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