Galaxy models predict a tight relation between the clustering of galaxies and dark matter on cosmological scales, but predictions differ notably in the details. We used this opportunity and tested… Click to show full abstract
Galaxy models predict a tight relation between the clustering of galaxies and dark matter on cosmological scales, but predictions differ notably in the details. We used this opportunity and tested two semi-analytic models by the Munich and Durham groups with data from the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). For the test we measured the scale-dependent galaxy bias factor b(k) and correlation factor r(k) from linear to non-linear scales of k ≈ 10 h Mpc−1 at two redshifts z̄ = 0.35, 0.51 for galaxies with stellar mass between 5 × 109 and 3 × 1011 h−2 70 M . Our improved gravitational lensing technique accounts for the intrinsic alignment of sources and the magnification of lens galaxies for better constraints for the galaxy-matter correlation r(k). Galaxy bias in CFHTLenS increases with k and stellar mass; it is colour-dependent, revealing the individual footprints of galaxy types. Despite a reasonable model agreement for the relative change with both scale and galaxy properties, there is a clear conflict for b(k) with no model preference: the model galaxies are too weakly clustered. This may flag a model problem at z & 0.3 for all stellar masses. As in the models, however, there is a high correlation r(k) between matter and galaxy density on all scales, and galaxy bias is typically consistent with a deterministic bias on linear scales. Only our blue and low-mass galaxies of about 7 × 109 h−2 70 M at z̄ = 0.51 show, contrary to the models, a weak tendency towards a stochastic bias on linear scales where rls = 0.75 ± 0.14 (stat.) ± 0.06 (sys.). This result is of interest for cosmological probes, such as EG, that rely on a deterministic galaxy bias. We provide Monte Carlo realisations of posterior constraints for b(k) and r(k) in CFHTLenS for every galaxy sample in this paper as online material.
               
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