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24 April 2024 |
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Article overview
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Beyond Kaiser bias: mildly non-linear two-point statistics of densities in distant spheres | | C. Uhlemann
; S. Codis
; J. Kim
; C. Pichon
; F. Bernardeau
; D. Pogosyan
; C. Park
; B. L'Huillier
; | | Date: |
4 Jul 2016 | | Abstract: | Simple parameter-free analytic bias functions for the two-point correlation
of densities in spheres at large separation are presented. These bias functions
generalize the so-called Kaiser bias to the mildly non-linear regime for
arbitrary density contrasts. The derivation is carried out in the context of
large deviation statistics while relying on the spherical collapse model. A
logarithmic transformation provides a saddle approximation which is valid for
the whole range of densities and shown to be accurate against the 30 Gpc cube
state-of-the-art Horizon Run 4 simulation. Special configurations of two
concentric spheres that allow to identify peaks are employed to obtain the
conditional bias and a proxy to BBKS extrema correlation functions. These
analytic bias functions should be used jointly with extended perturbation
theory to predict two-point clustering statistics as they capture the
non-linear regime of structure formation at the percent level down to scales of
about 10 Mpc/h at redshift 0. Conversely, the joint statistics also provide us
with optimal dark matter two-point correlation estimates which can be applied
either universally to all spheres or to a restricted set of biased (over- or
underdense) pairs. Based on a simple fiducial survey, this estimator is shown
to perform five times better than usual two-point function estimators.
Extracting more information from correlations of different types of objects
should prove essential in the context of upcoming surveys like Euclid, DESI,
PFS or LSST. | | Source: | arXiv, 1607.1026 | | Services: | Forum | Review | PDF | Favorites | |
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