Studying Galaxy Formation and Evolution with New Data, Models, and Methods

University Of California-Santa Cruz, Santa Cruz CA

Investigators

Abstract

AST 0205944 Primack The goals of this project are to improve the understanding of key issues in galaxy formation and evolution, especially the origin of the structure of dark matter halos and the evolution of their baryonic contents. It will address the questions that have arisen concerning cold dark matter (CDM) type models by developing analytic and semi-analytic treatments based on, and tested against, high-resolution simulations. These will provide detailed predictions of galaxy properties at both high and low redshifts, to be compared to space and ground based data especially from the Deep Extragalactic Evolutionary Probe (DEEP) program. These simulations feature use of the Adaptive Refinement Tree (ART) technique, which adjusts the resolution automatically to follow particles in dense regions, and can now use particles of different masses to improve mass and force resolution. This permits the identification of all dark matter halos that can harbor bright galaxies. In addition the project will use a second tool, Semi-Analytic Merging (SAM) to model the outcomes of various star formation and feedback scenarios in CDM galaxy formation. These SAM models will be used to study the structural and chemical evolution of galaxies, including gas cooling, star formation, supernova feedback, satellite merging, and the effects of dust. They model dust emission, and allow the prediction of galaxy counts and integrated backgrounds over wavelengths from the far-UV to the sub-mm. These predictions will be invaluable for interpreting data from previous and planned space missions such as COBE, SIRTF, GALEX, etc. The computer codes for the ART and SAM models will be made available to the public. .***