Measuring the Nonlinear Biasing Function from a Galaxy Redshift Survey

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.Comment: 23 pages, 7 figures, revised version, accepted for publication in Ap

IRAS versus POTENT Density Fields on Large Scales: Biasing and Omega

The galaxy density field as extracted from the IRAS 1.2 Jy redshift survey is compared to the mass density field as reconstructed by the POTENT method from the Mark III catalog of peculiar velocities. The reconstruction is done with Gaussian smoothing of radius 12 h^{-1}Mpc, and the comparison is carried out within volumes of effective radii 31-46 h^{-1}Mpc, containing approximately 10-26 independent samples. Random and systematic errors are estimated from multiple realizations of mock catalogs drawn from a simulation that mimics the observed density field in the local universe. The relationship between the two density fields is found to be consistent with gravitational instability theory in the mildly nonlinear regime and a linear biasing relation between galaxies and mass. We measure beta = Omega^{0.6}/b_I = 0.89 \pm 0.12 within a volume of effective radius 40 h^{-1}Mpc, where b_I is the IRAS galaxy biasing parameter at 12 h^{-1}Mpc. This result is only weakly dependent on the comparison volume, suggesting that cosmic scatter is no greater than \pm 0.1. These data are thus consistent with Omega=1 and b_I\approx 1. If b_I>0.75, as theoretical models of biasing indicate, then Omega>0.33 at 95% confidence. A comparison with other estimates of beta suggests scale-dependence in the biasing relation for IRAS galaxies.Comment: 35 pages including 10 figures, AAS Latex, Submitted to The Astrophysical Journa

Determining the Biasing Function from Galaxy Redshift Surveys

We present a method for evaluating the biasing function of galaxies, delta (g)(delta), from a redshift survey from the cumulative distribution functions of galaxies and mass, C-g(delta (g)) and C(delta), respectively. Using a suite of N-body simulations of different cosmological models, we show how the nonlinear biasing function can be obtained with adequate accuracy directly from the observed counts in cells in redshift space under the assumption that C(delta) can be approximated by a cumulative log-normal distribution in 1+delta, Once applied to redshift surveys such as PSCz, 2dF, or SDSS the biasing function carl provide valuable constraints on galaxy formation and structure evolution.</p