EVOLUTION OF STRUCTURE IN COLD DARK-MATTER UNIVERSES

We present an analysis of the clustering evolution of dark matter in f our cold dark matter (CDM) cosmologies. We use a suite of high-resolut ion, 17 million particle, N-body simulations that sample volumes large enough to give clustering statistics with unprecedented accuracy. We investigate a flat model with Omega(0) = 0.3, an open model also with Omega(0) = 0.3, and two models with Omega = 1, one with the standard C DM power spectrum and the other with the same power spectrum as the Om ega(0) = 0.3 models. In all cases, the amplitude of primordial fluctua tions is set so that the models reproduce the observed abundance of ri ch galaxy clusters by the present day. We compute mass two-point corre lation functions and power spectra over 3 orders of magnitude in spati al scale and find that in all of our simulations they differ significa ntly from those of the observed galaxy distribution, in both shape and amplitude. Thus, for any of these models to provide an acceptable rep resentation of reality, the distribution of galaxies must be biased re lative to the mass in a nontrivial, scale-dependent fashion. In the Om ega = 1 models, the required bias is always greater than unity, but in the Omega(0) = 0.3 models, an ''antibias'' is required on scales smal ler than similar to 5 h(-1) Mpc. The mass correlation functions in the simulations are well fit by recently published analytic models. The v elocity fields are remarkably similar in all the models, whether they are characterized as bulk flows, single-particle, or pairwise velocity dispersions. This similarity is a direct consequence of our adopted n ormalization and runs contrary to the common belief that the amplitude of the observed galaxy velocity fields can be used to constrain the v alue of Omega(0). The small-scale pairwise velocity dispersion of the dark matter is somewhat larger than recent determinations from galaxy redshift surveys, but the bulk Bows predicted by our models are broadl y in agreement with most available data.