Bibcode
Kitaura, F.-S.; Gil-Marín, Héctor; Scóccola, C. G.; Chuang, Chia-Hsun; Müller, Volker; Yepes, Gustavo; Prada, Francisco
Referencia bibliográfica
Monthly Notices of the Royal Astronomical Society, Volume 450, Issue 2, p.1836-1845
Fecha de publicación:
6
2015
Número de citas
65
Número de citas referidas
59
Descripción
We present a method to produce mock galaxy catalogues with efficient
perturbation theory schemes, which match the number density, power
spectra and bispectra in real and in redshift space from N-body
simulations. The essential contribution of this work is the way in which
we constrain the bias parameters of the PATCHY-code. In addition to
aiming at reproducing the two-point statistics, we seek the set of bias
parameters, which constrain the univariate halo probability distribution
function (PDF) encoding higher order correlation functions. We
demonstrate that halo catalogues based on the same underlying dark
matter field with a fix halo number density, and accurately matching the
power spectrum (within 2 per cent) can lead to very different bispectra
depending on the adopted halo bias model. A model ignoring the shape of
the halo PDF can lead to deviations up to factors of 2. The catalogues
obtained additionally constraining the shape of the halo PDF can
significantly lower the discrepancy in the three-point statistics,
yielding closely unbiased bispectra both in real and in redshift space;
which are in general compatible with those corresponding to an N-body
simulation within 10 per cent (deviating at most up to 20 per cent). Our
calculations show that the constant linear bias of ˜2 for luminous
red galaxy (LRG) like galaxies found in the power spectrum, mainly comes
from sampling haloes in high-density peaks, choosing a high-density
threshold rather than from a factor multiplying the dark matter density
field. Our method contributes towards an efficient modelling of the
halo/galaxy distribution required to estimate uncertainties in the
clustering measurements from galaxy redshift surveys. We have also
demonstrated that it represents a powerful tool to test various bias
models.