<p> Through their connection with dark matter structures, galaxies act as tracers of the underlying matter distribution in the Universe. Their observed spatial distribution allows us to precisely measure large scale structure and effectively test cosmological models that explain the content, geometry, and history of the Universe. Current observations from galaxy surveys such as the Baryon Oscillation Spectroscopic Survey have already probed vast cosmic volumes with millions of galaxies and ushered in an era of precision cosmology. The next surveys will probe over an order of magnitude more. With this unprecedented statistical power, the bottleneck of scientific discovery is in the methodology. </p><p> In this dissertation, I address major methodological challenges in constraining cosmology with the large-scale distribution of galaxies. I develop a robust framework for treating systematic effects, which significantly bias galaxy clustering measurements. I apply new innovative approaches to probabilistic parameter inference that challenge and test the in- correct assumptions of the standard approach. Furthermore, I use precise predictions of structure formation from cosmology and observations of galaxies during the last eight billion years to develop detailed models of how galaxies are impacted by their host dark matter structures. These models provide key insight into the galaxy-halo connection, which bridges the gap between cosmology theory and observations. They also answer crucial questions of how galaxies form and evolve. The developments in this dissertation will help unlock the full potential of future observations and allow us to precisely test cosmological models, General Relativity and modified gravity scenarios, and even particle physics theory beyond the Standard Model.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10261676 |
| Date | 14 September 2017 |
| Creators | Hahn, ChangHoon |
| Publisher | New York University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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