## Type of Work

Article (6) Book (0) Theses (3) Multimedia (0)

## Peer Review

Peer-reviewed only (9)

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## Publication Year

## Campus

UC Berkeley (4) UC Davis (0) UC Irvine (0) UCLA (0) UC Merced (0) UC Riverside (0) UC San Diego (0) UCSF (0) UC Santa Barbara (0) UC Santa Cruz (1) UC Office of the President (1) Lawrence Berkeley National Laboratory (4) UC Agriculture & Natural Resources (0)

## Department

Research Grants Program Office (RGPO) (1) University of California Research Initiatives (UCRI) (1) Multicampus Research Programs and Initiatives (MRPI); a funding opportunity through UC Research Initiatives (UCRI) (1)

## Journal

## Discipline

Physical Sciences and Mathematics (2)

## Reuse License

## Scholarly Works (9 results)

The analysis of the large-scale structure (LSS) of the Universe can yield insights into some of the most important questions in contemporary cosmology, and in recent years, has become a data-driven endeavor. With ever-growing data sets, optimal analysis techniques have become essential, not only to extract statistics from data, but also to effectively use computing resources to produce accurate theoretical predictions for those statistics. Future LSS experiments will help answer fundamental questions about our Universe, including the physical nature of dark energy, the mass scale of neutrinos, and the physics of inflation. To do so, improvements must be made to theoretical models as well as the computational tools used to perform such analyses.

This thesis examines multiple aspects of LSS data analysis, presenting novel modeling techniques as well as a software toolkit suitable for analyzing data from the next generation of LSS surveys. First, we present nbodykit, an open-source, massively parallel Python toolkit for analyzing LSS data. nbodykit is both an interactive and scalable piece of scientific software, providing parallel implementations of many commonly used algorithms in LSS. Its modular design allows researchers to integrate nbodykit with their own software to build complex applications to solve specific problems in LSS. Next, we derive an optimal means of using fast Fourier transforms to estimate the multipoles of the line-of-sight dependent power spectrum, eliminating redundancy present in previous estimators in the literature. We also discuss potential advantages of our estimator for future data sets. We then present a novel theoretical model for the redshift-space galaxy power spectrum and demonstrate its accuracy in describing the clustering of galaxies down to scales of k = 0.4 h/Mpc. Finally, we analyze the large-scale clustering of quasars from the extended Baryon Oscillation Spectroscopic Survey to constrain the deviation from Gaussian random field initial conditions in the early Universe, known as primordial non-Gaussianity.

The study of large-scale structure aims to describe the distribution of matter in the universe. This thesis examines two important topics in this field: intrinsic alignments and anisotropic galaxy clustering. Intrinsic alignments of galaxies are correlations between galaxy shapes and the surrounding density field and are a significant systematic uncertainty in weak gravitational lensing measurements. We examine intrinsic alignments from both theoretical and observational perspectives. First, the most commonly used model for intrinsic alignments, the tidal (linear) alignment model, is tested and developed in the context of recent measurements. The model is found to provide a reasonably accurate description of observations on large scales, particularly for luminous red galaxies. We raise unresolved issues with the model and explore several ways in which it could be improved and expanded. Second, we develop a technique to separate intrinsic alignments from galaxy-galaxy lensing measurements. This technique allows the removal of contamination from the desired lensing signal while also providing a probe of intrinsic alignments in different galaxy populations. Using data from the Sloan Digital Sky Survey (SDSS), we use this method to place the tightest current constraints on intrinsic alignment strength for the SDSS lensing sample, finding that it is a subdominant source of uncertainty at the current level of statistical precision.

The measured galaxy clustering signal in redshift surveys is anisotropic due to both geometric and dynamic distortions. We discuss the nature of these distortions and examine their information content in the context of future redshift surveys. We employ a model for the galaxy power spectrum in redshift space, based on an expansion in velocity moments of the phase-space distribution function of matter. This model includes nonlinear treatment of matter clustering, galaxy bias, shot noise, and redshift-space distortions. Comparing with numerical simulations, we find that this model is able to describe halo clustering with sufficient accuracy to allow the inclusion of broadband information on small scales, thus significantly improving constraints on both geometry and growth of structure in the universe.

This thesis presents a systematic study of the relationships between galaxy properties and their environments. Multi-wavelength imaging and spectroscopic data are used to construct a large sample of galaxy groups spanning nearly half the age of the Universe. Weak gravitational lensing measurements enable a census of baryonic and dark matter in these groups, and in particular, identification of halo centers. With empirically determined centers of mass, trends in galaxy colors and morphologies with groupcentric distance are studied to test models of galaxy evolution.