A question of separation: disentangling tracer bias and gravitational non-linearity with counts-in-cells statistics

Journal article

Starting from a very accurate model for density-in-cells statistics of dark matter based on large deviation theory, a bias model for the tracer density in spheres is formulated. It adopts a mean bias relation based on a quadratic bias model to relate the log-densities of dark matter to those of mass-weighted dark haloes in real and redshift space. The validity of the parametrized bias model is established using a parametrization-independent extraction of the bias function. This average bias model is then combined with the dark matter PDF, neglecting any scatter around it: it nevertheless yields an excellent model for densities-in-cells statistics of mass tracers that is parametrized in terms of the underlying dark matter variance and three bias parameters. The procedure is validated on measurements of both the one- and twopoint statistics of subhalo densities in the state-of-the-art Horizon Run 4 simulation showing excellent agreement for measured dark matter variance and bias parameters. Finally, it is demonstrated that this formalism allows for a joint estimation of the non-linear dark matter variance and the bias parameters using solely the statistics of subhaloes. Having verified that galaxy counts in hydrodynamical simulations sampled on a scale of 10 Mpc h-1 closely resemble those of subhaloes, this work provides important steps towards making theoretical predictions for density-in-cells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST.

Authors: Uhlemann, C; Feix, M; Codis, S; Pichon, C; Bernardeau, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Oxford University Press
• Journal: Monthly Notices of the Royal Astronomical Society
• Volume: 473
• Issue: 4
• Pages: 5098-5112
• Publication date: 2017-10-09
• Acceptance date: 2017-10-02
• DOI: 10.1093/mnras/stx2616
• EISSN: 1365-2966
• ISSN: 0035-8711
• Language: English
• Keywords: FFR