Dissertation/Thesis Abstract

Search, Identification, and Study of Galactic Compact Objects: Methods, Environments, and Populations
by Hare, Jeremy, Ph.D., The George Washington University, 2019, 254; 10974738
Abstract (Summary)

There are a large number of interesting astrophysical objects in the Galaxy. However, few push the limits of our understanding of physics under extreme conditions like neutron stars and black holes. Studying these objects, and how they interact with their environments, is critical to understanding their populations, evolution, and the physical processes associated with these extreme objects. In order to obtain an overview of the diverse properties and populations of compact objects, I first searched for and studied them in a variety of different astrophysical environments, including extended TeV sources, stellar clusters, supernova remnants, and the Galactic disk. This has exposed me to the various challenges in the identification of compact objects. It has also allowed me to understand the physical processes underlying the very different observational manifestations of compact objects. In some cases, such as the study of the gamma-ray binary LS 2883, an unexpected astrophysical phenomenon was discovered, namely, the ejection of matter with a relativistic velocity from the binary. In other cases, such as the massive cluster Glimpse-C01, the limitations of the currently avail- able (and forthcoming) instrumentation and observations have been demonstrated. These studies have reinforced the fact that automated and robust tools for the classification of different types of astrophysical sources, emitting at high energies, will play a critical role in the era of large scale multi-wavelength datasets. Therefore, we develop and use a multi-wavelength machine-learning pipeline to classify X-ray sources in a number of different environments, with the goal of increasing the population of known compact objects and to search for outlying sources.

Indexing (document details)
Advisor: Kargaltsev, Oleg
Commitee: Corcoran, Michael, Doering, Michael, Guiriec, Sylvain, van der Horst, Alexander
School: The George Washington University
Department: Physics
School Location: United States -- District of Columbia
Source: DAI-B 80/05(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Astrophysics, Physics
Keywords: High mass gamma-ray binaries, Machine learning, Neutron stars, Stellar clusters
Publication Number: 10974738
ISBN: 9780438769618
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