| During the last decade, there has been ever increasing interest in the problem of galaxy formation in a cosmological context. Detailed studies of this phenomenon require three-dimensional numerical simulations of very high dynamic range. We have developed the Adaptive Refinement Tree (ART) numerical algorithm for high-resolution dissipationless and gasdynamical cosmological simulations. The algorithm follows particle trajectories and solves the equations of gasdynamics on both a cubic, uniform grid covering the entire computational volume and on finer meshes, introduced recursively in a fully adaptive manner in regions of interest. The refinement meshes are generated to effectively match an arbitrary geometry of the interesting regions, a property particularly important for cosmological simulations. In this thesis, I present a description of the method and its implementation, tests of the numerical code, and two applications which effectively demonstrate the high-resolution capabilities of the code. The first study concerns the abundances of galactic satellites in hierarchical models of structure formation. We find that the theoretical models predict much larger numbers of satellites around Milky Way-type galaxies than are actually observed. We then discuss several possible explanations for the differences in predicted and observed velocity functions, including the identification of some satellites with High-Velocity Clouds observed in the Local Group, and the existence of dark satellites which failed to accrete gas and form stars, due either to the expulsion of gas in the supernovae-driven winds or to gas heating by the intergalactic ionizing background. The second study concerns the problem of the galaxy clustering bias, the difference between the galaxy distribution and the overall distribution of matter, and its evolution with time. We use several statistics to study the bias evolution and find that in general, the bias is nonlinear and time- and scale-dependent. Nevertheless, we show that despite the apparent complexity, the origin and evolution of bias can be understood in terms of the processes that drive the formation and evolution of dark matter halos. These processes conspire to produce a halo distribution quite different from the overall distribution of matter, yet remarkably similar to the observed distribution of galaxies. |
