Galaxy clustering from the bottom up: a streaming model emulator I

Abstract

In this series of papers, we present a simulation-based model for the non-linear clustering of galaxies based on separate modelling of clustering in real space and velocity statistics. In the first paper, we present an emulator for the real-space correlation function of galaxies, whereas the emulator of the real-to-redshift space mapping based on velocity statistics is presented in the second paper. Here, we show that a neural network emulator for real-space galaxy clustering trained on data extracted from the dark quest suite of N-body simulations achieves sub-per cent accuracies on scales 1 < r < 30 h-1 Mpc, and better than 3 per cent on scales r < 1 h-1 Mpc in predicting the clustering of dark-matter haloes with number density 10-3.5 (h-1 Mpc)-3, close to that of SDSS LOWZ-like galaxies. The halo emulator can be combined with a galaxy-halo connection model to predict the galaxy correlation function through the halo model. We demonstrate that we accurately recover the cosmological and galaxy-halo connection parameters when galaxy clustering depends only on the mass of the galaxies' host halos. Furthermore, the constraining power in σ8 increases by about a factor of 2 when including scales smaller than 5h-1 Mpc. However, when mass is not the only property responsible for galaxy clustering, as observed in hydrodynamical or semi-analytic models of galaxy formation, our emulator gives biased constraints on σ8. This bias disappears when small scales (r < 10 h-1 Mpc) are excluded from the analysis. This shows that a vanilla halo model could introduce biases into the analysis of future data sets.