UC San Diego

In optical wavelengths, galaxies are observed to be either red or blue. The overall color of a galaxy is due to the distribution of the ages of its stellar population. Galaxies with currently active star formation appear blue, while those with no recent star formation at all (greater than about a Gyr) have only old, red stars. This strong bimodality has lead to the idea of star formation quenching, and various proposed physical mechanisms. In this dissertation, I attempt to reproduce with Enzo the results of Naab et al. (2007), in which red and dead galaxies are formed using gravitational quenching,rather than with one of the more typical methods of quenching. My initial attempts are unsuccessful, and I explore the reasons why I think they failed. Then using simpler methods better suited to Enzo + AMR, I am successful in producing a galaxy that appears to be similar in color and formation history to those in Naab et al. However, quenching is achieved using unphysically high star formation efficiencies, which is a different mechanism than Naab et al. suggests. Preliminary results of a much higher resolution, follow-on simulation of the above show some possible contradiction with the results of Naab et al. Cold gas is streaming into the galaxy to fuel starbursts, while at a similar epoch the galaxies in Naab et al. have largely already ceased forming stars in the galaxy. On the other hand, the results of the high resolution simulation are qualitatively similar to other works in the literature that show a somewhat different gravitational quenching mechanism than Naab et al. I also discuss my work using halo finders to analyze simulated cosmological data, and my work improving the Enzo/AMR analysis tool "yt". This includes two parallelizations of the halo finder HOP which allows analysis of very large cosmological datasets on parallel machines. The first version is "yt-HOP," which works well for datasets between about 256³ and 512³ particles, but has memory bottlenecks as the datasets get larger. These bottlenecks inspired the second version, "Parallel HOP," which is a fully parallelized method and implementation of HOP that has worked on datasets with more than 2048³ particles on hundreds of processing cores. Both methods are described in detail, as are the various effects of performance- related runtime options. Additionally, both halo finders are subjected to a full suite of performance benchmarks varying both dataset sizes and computational resources used. I conclude with descriptions of four new tools I added to yt. A Parallel Structure Function Generator allows analysis of two-point functions, such as correlation functions, using memory- and workload- parallelism. A Parallel Merger Tree Generator leverages the parallel halo finders in yt, such as Parallel HOP, to build the merger tree of halos in a cosmological simulation, and outputs the result to a SQLite database for simple and powerful data extraction. A Star Particle Analysis toolkit takes a group of star particles and can output the rate of formation as a function of time, and/or a synthetic Spectral Energy Distribution (S.E.D.) using the Bruzual and Charlot (2003) data tables. Finally, a Halo Mass Function toolkit takes as input a list of halo masses and can output the halo mass function for the halos, as well as an analytical fit for those halos using several previously published fits