Galaxy clustering from the bottom up: A Streaming Model emulator I
arXiv:2208.05218 · doi:10.1093/mnras/stad1207
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 , and better than on scales in predicting the clustering of dark-matter haloes with number density , 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 increases by about a factor of when including scales smaller than . 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 . This bias disappears when small scales ( ) are excluded from the analysis. This shows that a vanilla halo model could introduce biases into the analysis of future datasets.
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