The goal of modern cosmology is to understand the behavior of the universe at large scales, including the evolution of the matter distribution over cosmic time. Advances in this endeavor require both better characterization of systematic errors in raw astronomical data as well as improved statistical methods for extracting cosmological information from the galaxy catalogs produced by modern surveys. Toward these ends, I will begin by demonstrating a method for inferring the impact of pixel grid distortions in CCD imagers on fundamental astronomical observables. I will then present the results from applying this method to data from both the Dark Energy Survey (DES) and prototype sensors for the Large Synoptic Survey Telescope (LSST). In the second half of my talk, I will discuss a novel method for constraining linear and non-linear galaxy bias in photometric survey data using the galaxy three-point correlation function. This is a statistical probe of the "cosmic web" of dark matter, one that enables the extraction of more cosmological information from the observed galaxy distribution than is accessible with traditional two-point observables. I will show that the three-point correlation function is a useful probe of galaxy bias even in the presence of significant photometric redshift uncertainties. Both threads of this thesis will be useful in ongoing and upcoming analyses of data from photometric galaxy surveys, including DES and LSST.
Speaker: Michael Baumer
Thesis defense. Room 102/103
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