Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Physics & Astronomy

First Advisor

Bhuvnesh Jain


Two-point statistics can be used to probe various types of cosmic structures. We perform a cross-correlation measurement using $\sim 160,000$ red satellite galaxies in SDSS redMaPPer clusters and find evidence that subhalo correlations do persist well beyond their tidal radius, suggesting that many of the observed satellites fell into their current host less than a dynamical time ago, $t_{\rm{infall}} < t_{\rm{dyn}}$. Combined with estimated dynamical times $t_{\rm{dyn}} \sim 3 - 5$ Gyr and SED fitting results for the time at which satellites stopped forming stars, $t_{\rm{quench}} \sim 6$ Gyr, we infer that for a significant fraction of the satellites, star formation quenched before those satellites entered their current hosts. In addition to cross-correlation measurement, galaxy-galaxy lensing is another powerful two-point statistic in cosmology analysis. We introduce and perform a number of tests of systematics on the DES Science Verification shear catalogs and photometric redshifts. We estimate the covariance matrices for the DES Year 1 galaxy-galaxy lensing measurement using the Jackknife approach. We validate the estimation using a suite of log-normal mock surveys. After testing the pipeline of our weak lensing measurement, we perform comprehensive measurements of weak lensing and galaxy clustering around voids in DES Year 1 data. We get heretofore the highest signal-to-noise void lensing measurements for voids identified by two different void finding algorithms. Using data from the MICE simulation, we study the impact of photo-z scatter on watershed types of void finder. We show that the photo-z scatter has introduced a selection bias which results in a boosting of the negative lensing signal. We also combine our observables of void lensing and void-galaxy cross-correlation to test the linear bias of redMaGiC galaxies distributed around voids. We see no evidence of departure from linearity.

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