Publication : t17/181

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

Uhlemann C. (Institute for Theoretical Physics, Utrecht University, 3508 TD Utrecht, The Netherlands)
Feix M. ()
Pichon C. ()
Bernardeau F. (CEA, IPhT (Institut de Physique Théorique), F-91191 Gif-sur-Yvette, France)
L Huillier B. ()
Kim J. ()
Laigle C. ()
Park C. ()
Shin J. ()
Pogosyan D. ()
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 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 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 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.
Année de publication : 2018
Revue : Mon. Not. R. Astron. Soc. 473 1 (2018)
DOI : 10.1093/mnras/stx2616
Langue : Anglais


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