Date of Award

Spring 1-1-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Astronomy

First Advisor

van Dokkum, Pieter

Abstract

Understanding the formation and evolution of galaxies across cosmic time requires both observations across redshift, wavelength, and method as well as sophisticated machinery for interpreting and carrying out inference on observations. Techniques such Bayesian Inference, Simulation Based Inference, and Machine learning are seeing increased use as attempts are made to extract meaningful results from challenging data. Meanwhile new instrumentation, from spectrographs on major telescopes (e.g., KCWI and MUSE) to new, novel designs (e.g., the Mothra Telescope Array) are providing new observational windows into heretofore invisible portions of the baryon cycle --- a fundamental cycle driving and describing how baryons move throughout the universe and into and out of galaxies. This thesis presents the development and application of statistical techniques and novel instrumentation to infer the properties of galaxies and their place within the baryon cycle.

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