Date of Award

January 2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Physics & Astrophysics

First Advisor

Wayne Barkhouse

Abstract

The classification of galaxies based on their morphology (i.e. structural properties) is a field in astrophysics that aims to understand galaxy formation and evolution based on their physical differences. Whether structural differences are due to internal factors or a result of local environment, the dominate mechanism that determines galaxy type needs to be robustly quantified in order to have a thorough grasp of the origin of the different types of galaxies (e.g., elliptical, S0, spiral, and irregular). The main subject of this thesis is to explore the use of computers to automatically analyze and classify large numbers of galaxies based on their morphology, and to analyze sub-samples of galaxies selected by type to understand galaxy formation and evolution in various environments. I have developed computer software to classify galaxies by measuring specific parameters extracted from digital images. In particular, I have constructed computer algorithms to calculate five classification parameters for a list of galaxies in a single FITS image. This research has important implications for increasing our knowledge of galaxy formation and evolution in dense systems. A diverse range of data sets is studied, primarily focusing on: Rude (2015), Barkhouse et al. (2007), WINGS (Fasano et al. 2006), and Baillard et al. (2011). The data sets include galaxies from a wide range of redshifts, from 0.03 ≤ z ≤ 0.20. The different span of redshift allows for comparison of distant clusters with those nearby in order to look for evolutionary changes in the galaxy cluster population.

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