http://arxiv.org/abs/1708.04892
Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models in a statistical sense. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the “accurate” regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. An advantage of this approach is that it can easily incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard $\Lambda\mathrm{CDM}$ + halo model against the clustering of SDSS DR7 galaxies. Specifically, we use the projected correlation function, group multiplicity function and galaxy number density as constraints. We find that while the model provides a good match to each statistic separately, it struggles to fit them jointly. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir halos) matches the clustering of low luminosity galaxies, but exhibits a $2.3 \sigma$ tension with the clustering of luminous galaxies, thus providing evidence that the “standard” halo model needs to be extended. This work represents the most accurate modelling of small-scale clustering to-date and opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.
M. Sinha, A. Berlind, C. McBride, et. al.
Thu, 17 Aug 17
41/50
Comments: 25 pages, 12 figures. Full-resolution paper at this http URL To be submitted to MNRAS soon, comments are welcome