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A. V. Kravtsov, A. A. Klypin (NMSU), A. M. Khokhlov (NRL)
In hierarchical structure formation scenarios, dark matter (DM) halos are expected to host luminous galaxies. The spatial distribution of halos may, in general, be biased with respect to the overall matter distribution. Thus, understanding this bias and its evolution are of primary importance for interpretation of the galaxy clustering data at low and high redshifts. Here, we present the results from a detailed study of halo bias in a large, high-resolution cosmological N-body simulation of a popular \LambdaCDM model. The simulation, done with the Adaptive Refinement Tree (ART) N-body code, followed the evolution of about 16.7 million particles in a 60h-1= 85 {\rm Mpc} box, with peak force resolution of about 2-3h-1 kpc (dynamic range of 32000). The high spatial and mass resolution allows us to resolve halos even in high-density regions corresponding to galaxy groups and clusters.
We give an overview of the ART code, present results on the evolution of the matter and halo two-point correlation functions and power spectra, and compare these statistics with the statistics of the observed galaxy distribution at different redshifts. We find that our results agree well with all of the existing clustering data, indicating the general success of hierarchical galaxy formation models. Particularly, we find excellent agreement, in both shape and amplitude, between the {\em halo} correlation function and power spectrum in our \LambdaCDM simulation and the galaxy correlation function and spectrum measured using the APM galaxy survey. At high redshifts, the observed clustering of the Lyman-break galaxies is also well reproduced by the model. We show that distribution of halos is {\em biased nonlinearly} with respect to the matter distribution. It is therefore crucial to consider the distribution of halos, not matter, when comparing model predictions to the observations.
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The author(s) of this abstract have provided an email address for comments about the abstract: akravtso@nmsu.edu