A question of separation: disentangling tracer bias and gravitational
  nonlinearity with counts-in-cells statistics
  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 parametrised bias model
is established using a parametrisation-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 parametrised in terms of
the underlying dark matter variance and three bias parameters. The procedure is
validated on measurements of both the one and two point 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 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 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.
