A question of separation: disentangling tracer bias and gravitational nonlinearity with countsincells statistics
Abstract
Starting from a very accurate model for densityincells 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 logdensities of dark matter to those of massweighted dark haloes in real and redshift space. The validity of the parametrized bias model is established using a parametrizationindependent 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 densitiesincells 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 stateoftheart 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 nonlinear 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 densityincells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 February 2018
 DOI:
 10.1093/mnras/stx2616
 arXiv:
 arXiv:1705.08901
 Bibcode:
 2018MNRAS.473.5098U
 Keywords:

 largescale structure of Universe;
 cosmology: theory;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies
 EPrint:
 14 pages, 11 figures