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We present a method of including galaxy formation in dissipationless N-body simulations. Galaxies that form during the evolution are identified at several epochs and replaced by single massive soft particles. This allows one to produce two-component models containing galaxies and a background dark matter distribution. We applied this technique to obtain two sets of models: one for field galaxies and one for galaxy clusters. We tested the method for the standard CDM scenario for structure formation in the universe. A direct comparison of the simulated galaxy distribution to the observed one sets the amplitude of the initial density fluctuation spectrum, and thus the present time in the simulations. The rates of formation and merging compare very well to simulations that include hydrodynamics, and are compatible with observations. We also discuss the cluster luminosity function.
Accurate modeling of galaxy formation in a hierarchical, cold dark matter universe requires the use of sufficiently high-resolution merger trees to obtain convergence in the predicted properties of galaxies. When semi-analytic galaxy formation models
We use high-resolution dissipationless simulations of the concordance flat LCDM model to make predictions for the galaxy--mass (GM) correlations and compare them to the recent SDSS weak lensing measurements.We use a simple observationally motivated s
We examine two extreme models for the build-up of the stellar component of luminous elliptical galaxies. In one case, we assume the build-up of stars is dissipational, with centrally accreted gas radiating away its orbital and thermal energy; the dar
We construct the Numerical Galaxy Catalog ($ u$GC), based on a semi-analytic model of galaxy formation combined with high-resolution N-body simulations in a $Lambda$-dominated flat cold dark matter ($Lambda$CDM) cosmological model. The model includes
We generate mock galaxy catalogues for a grid of different cosmologies, using rescaled N-body simulations in tandem with a semi-analytic model run using consistent parameters. Because we predict the galaxy bias, rather than fitting it as a nuisance p