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

Abstract : 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.
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Preprints, Working Papers, ...
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https://hal-cea.archives-ouvertes.fr/cea-01548491
Contributor : Emmanuelle de Laborderie <>
Submitted on : Tuesday, June 27, 2017 - 4:40:39 PM
Last modification on : Wednesday, August 19, 2020 - 12:08:19 PM

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  • HAL Id : cea-01548491, version 1
  • ARXIV : 1705.08901

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Cora Uhlemann, Martin Feix, Sandrine Codis, Christophe Pichon, Francis Bernardeau, et al.. A question of separation: disentangling tracer bias and gravitational nonlinearity with counts-in-cells statistics. 2017. ⟨cea-01548491⟩

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