TL;DR
HALOGEN is a fast, flexible tool that generates approximate synthetic halo catalogues using 2LPT, analytical mass functions, and stochastic bias modeling, accurately reproducing key clustering statistics for large-scale structure studies.
Contribution
This paper introduces HALOGEN, a simplified and efficient method for generating synthetic halo catalogues that accurately reproduce two-point functions and other statistics, suitable for large-scale survey analyses.
Findings
Reproduces 2-point function within 2% at 10-50 Mpc/h scales
Achieves 15% accuracy at ~100 Mpc/h scales
Demonstrates flexibility across different cosmologies and simulation setups
Abstract
We present a simple method of generating approximate synthetic halo catalogues: HALOGEN. This method uses a combination of -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 () scales, which we show is successful to within 2 per cent. Larger scales () 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…
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