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Register a new userScale-dependent Bias from the Reconstruction of Non-Gaussian Distributions
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Added by
Sirichai Chongchitnan
Publication date
2010
fields
Physics
and research's language is
English
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Primordial non-Gaussianity introduces a scale-dependent variation in the clustering of density peaks corresponding to rare objects. This variation, parametrized by the bias, is investigated on scales where a linear perturbation theory is sufficiently accurate. The bias is obtained directly in real space by comparing the one- and two-point probability distributions of density fluctuations. We show that these distributions can be reconstructed using a bivariate Edgeworth series, presented here up to an arbitrarily high order. The Edgeworth formalism is shown to be well-suited for local cubic-order non-Gaussianity parametrized by g_NL. We show that a strong scale-dependence in the bias can be produced by g_NL of order 10,000, consistent with CMB constraints. On correlation length of ~100 Mpc, current constraints on g_NL still allow the bias for the most massive clusters to be enhanced by 20-30% of the Gaussian value. We further examine the bias as a function of mass scale, and also explore the relationship between the clustering and the abundance of massive clusters in the presence of g_NL. We explain why the Edgeworth formalism, though technically challenging, is a very powerful technique for constraining high-order non-Gaussianity with large-scale structures.
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We measure the large-scale bias of dark matter halos in simulations with non-Gaussian initial conditions of the local type, and compare this bias to the response of the mass function to a change in the primordial amplitude of fluctuations. The two are found to be consistent, as expected from physical arguments, for three halo-finder algorithms which use different Spherical Overdensity (SO) and Friends-of-Friends (FoF) methods. On the other hand, we find that the commonly used prediction for universal mass functions, that the scale-dependent bias is proportional to the first-order Gaussian Lagrangian bias, does not yield a good agreement with the measurements. For all halo finders, high-mass halos show a non-Gaussian bias suppressed by 10-15% relative to the universal mass function prediction. For SO halos, this deviation changes sign at low masses, where the non-Gaussian bias becomes larger than the universal prediction.
Baryonic acoustic oscillations (BAOs) modulate the density ratio of baryons to dark matter across large regions of the Universe. We show that the associated variation in the mass-to-light ratio of galaxies should generate an oscillatory, scale-dependent bias of galaxies relative to the underlying distribution of dark matter. A measurement of this effect would calibrate the dependence of the characteristic mass-to-light ratio of galaxies on the baryon mass fraction in their large scale environment. This bias, though, is unlikely to significantly affect measurements of BAO peak positions.
The strong dependence of the large-scale dark matter halo bias on the (local) non-Gaussianity parameter, f_NL, offers a promising avenue towards constraining primordial non-Gaussianity with large-scale structure surveys. In this paper, we present the first detection of the dependence of the non-Gaussian halo bias on halo formation history using N-body simulations. We also present an analytic derivation of the expected signal based on the extended Press-Schechter formalism. In excellent agreement with our analytic prediction, we find that the halo formation history-dependent contribution to the non-Gaussian halo bias (which we call non-Gaussian halo assembly bias) can be factorized in a form approximately independent of redshift and halo mass. The correction to the non-Gaussian halo bias due to the halo formation history can be as large as 100%, with a suppression of the signal for recently formed halos and enhancement for old halos. This could in principle be a problem for realistic galaxy surveys if observational selection effects were to pick galaxies occupying only recently formed halos. Current semi-analytic galaxy formation models, for example, imply an enhancement in the expected signal of ~23% and ~48% for galaxies at z=1 selected by stellar mass and star formation rate, respectively.
We forecast the future constraints on scale-dependent parametrizations of galaxy bias and their impact on the estimate of cosmological parameters from the power spectrum of galaxies measured in a spectroscopic redshift survey. For the latter we assume a wide survey at relatively large redshifts, similar to the planned Euclid survey, as baseline for future experiments. To assess the impact of the bias we perform a Fisher matrix analysis and we adopt two different parametrizations of scale-dependent bias. The fiducial models for galaxy bias are calibrated using a mock catalogs of H$alpha$ emitting galaxies mimicking the expected properties of the objects that will be targeted by the Euclid survey. In our analysis we have obtained two main results. First of all, allowing for a scale-dependent bias does not significantly increase the errors on the other cosmological parameters apart from the rms amplitude of density fluctuations, $sigma_{8}$, and the growth index $gamma$, whose uncertainties increase by a factor up to two, depending on the bias model adopted. Second, we find that the accuracy in the linear bias parameter $b_{0}$ can be estimated to within 1-2% at various redshifts regardless of the fiducial model. The non-linear bias parameters have significantly large errors that depend on the model adopted. Despite of this, in the more realistic scenarios departures from the simple linear bias prescription can be detected with a $sim2,sigma$ significance at each redshift explored. Finally, we use the Fisher Matrix formalism to assess the impact of assuming an incorrect bias model and found that the systematic errors induced on the cosmological parameters are similar or even larger than the statistical ones.
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