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Register a new userStochastic Nonlinear Galaxy Biasing
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Avishai Dekel Hebrew U Jerusalem
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We propose a general formalism for galaxy biasing and apply it to methods for measuring cosmological parameters, such as regression of light versus mass, the analysis of redshift distortions, measures involving skewness and the cosmic virial theorem. The common linear and deterministic relation g=b*d between the density fluctuation fields of galaxies g and mass d is replaced by the conditional distribution P(g|d) of these random fields, smoothed at a given scale at a given time. The nonlinearity is characterized by the conditional mean <g|d>=b(d)*d, while the local scatter is represented by the conditional variance s_b^2(d) and higher moments. The scatter arises from hidden factors affecting galaxy formation and from shot noise unless it has been properly removed. For applications involving second-order local moments, the biasing is defined by three natural parameters: the slope b_h of the regression of g on d, a nonlinearity b_t, and a scatter s_b. The ratio of variances b_v^2 and the correlation coefficient r mix these parameters. The nonlinearity and the scatter lead to underestimates of order b_t^2/b_h^2 and s_b^2/b_h^2 in the different estimators of beta (=Omega^0.6/b_h). The nonlinear effects are typically smaller. Local stochasticity affects the redshift-distortion analysis only by limiting the useful range of scales, especially for power spectra. In this range, for linear stochastic biasing, the analysis reduces to Kaisers formula for b_h (not b_v), independent of the scatter. The distortion analysis is affected by nonlinear properties of biasing but in a weak way. Estimates of the nontrivial features of the biasing scheme are made based on simulations and toy models, and strategies for measuring them are discussed. They may partly explain the range of estimates for beta.
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It is well known that the clustering of galaxies depends on galaxy type.Such relative bias complicates the inference of cosmological parameters from galaxy redshift surveys, and is a challenge to theories of galaxy formation and evolution. In this paper we perform a joint counts-in-cells analysis on galaxies in the 2dF Galaxy Redshift Survey, classified by both colour and spectral type, eta, as early or late type galaxies. We fit three different models of relative bias to the joint probability distribution of the cell counts, assuming Poisson sampling of the galaxy density field. We investigate the nonlinearity and stochasticity of the relative bias, with cubical cells of side 10Mpc leq L leq 45Mpc (h=0.7). Exact linear bias is ruled out with high significance on all scales. Power law bias gives a better fit, but likelihood ratios prefer a bivariate lognormal distribution, with a non-zero `stochasticity - i.e. scatter that may result from physical effects on galaxy formation other than those from the local density field. Using this model, we measure a correlation coefficient in log-density space (r_LN) of 0.958 for cells of length L=10Mpc, increasing to 0.970 by L=45Mpc. This corresponds to a stochasticity sigma_b/bhat of 0.44pm0.02 and 0.27pm0.05 respectively. For smaller cells, the Poisson sampled lognormal distribution presents an increasingly poor fit to the data, especially with regard to the fraction of completely empty cells. We compare these trends with the predictions of semianalytic galaxy formation models: these match the data well in terms of overall level of stochasticity, variation with scale, and fraction of empty cells.
We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density fluctuation, or by the associated parameters of mean biasing and nonlinearity (following Dekel & Lahav 1999). Using the distribution of galaxies in cosmological simulations, at smoothing of a few Mpc, we find that the mean biasing can be recovered to a good accuracy from the cumulative distribution functions (CDFs) of galaxies and mass, despite the biasing scatter. Then, using a suite of simulations of different cosmological models, we demonstrate that the matter CDF is robust compared to the difference between it and the galaxy CDF, and can be approximated for our purpose by a cumulative log-normal distribution of 1+delta with a single parameter sigma. Finally, we show how the nonlinear biasing function can be obtained with adequate accuracy directly from the observed galaxy CDF in redshift space. Thus, the biasing function can be obtained from counts in cells once the rms mass fluctuation at the appropriate scale is assumed a priori. The relative biasing function between different galaxy types is measurable in a similar way. The main source of error is sparse sampling, which requires that the mean galaxy separation be smaller than the smoothing scale. Once applied to redshift surveys such as PSCz, 2dF, SDSS, or DEEP, the biasing function can provide valuable constraints on galaxy formation and structure evolution.
We perform an extensive analysis of nonlinear and stochastic biasing of galaxies and dark halos in spatially flat low-density CDM universe using cosmological hydrodynamic simulations. We compare their biasing properties with the predictions of an analytic halo biasing model. Dark halos in our simulations exhibit reasonable agreement with the predictions only on scales larger than 10h^{-1}Mpc, and on smaller scales the volume exclusion effect of halos due to their finite size becomes substantial. Interestingly the biasing properties of galaxies are better described by extrapolating the halo biasing model predictions. We also find the clear dependence of galaxy biasing on their formation epoch; the distribution of old populations of galaxies tightly correlates with the underlying mass density field, while that of young populations is slightly more stochastic and anti-biased relative to dark matter. The amplitude of two-point correlation function of old populations is about 3 times larger than that of the young populations. Furthermore, the old population of galaxies reside within massive dark halos while the young galaxies are preferentially formed in smaller dark halos. Assuming that the observed early and late-type galaxies correspond to the simulated old and young populations of galaxies, respectively, all of these segregations of galaxies are consistent with observational ones for the early and late-type of galaxies such as the morphology--density relation of galaxies.
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