Physics & Astronomy ETDs

Publication Date

Summer 7-28-2026

Abstract

Asymptotic giant branch (AGB) stars are among the final evolutionary stages of low- and intermediate-mass stars and among the most luminous cool stellar populations in the Milky Way (MW). Their strong infrared emission allows them to be observed through regions of high interstellar extinction, making them useful tracers of the inner MW. However, their use is limited by unreliable distances because many are dust-obscured and variable, while geometric parallaxes are often unavailable or uncertain. This thesis develops statistical distance-estimation methods for large samples of oxygen-rich AGB stars. The resulting distances broadly agree with literature estimates, supporting their statistical reliability for large AGB populations. These distances place AGB stars in a three-dimensional Galactic context and enable studies of Galactic structure through their spatial distributions, luminosities, variability, and population differences across the MW. In addition, this thesis applies machine learning to distance estimation and stellar classification, demonstrating its usefulness for separating stellar populations in large surveys. Overall, this work develops methods and catalogs that expand the use of AGB stars as Galactic tracers and provide distances for samples at least an order of magnitude larger than those accessible through traditional geometric techniques in the MW

Degree Name

Physics

Level of Degree

Doctoral

Department Name

Physics & Astronomy

First Committee Member (Chair)

Dr. Ylva Pihlstrom

Second Committee Member

Dr. Loránt Sjouwerman

Third Committee Member

Dr. Gregory Taylor

Fourth Committee Member

Prof. dr. Huib Jan van Langevelde

Language

English

Keywords

Milky Way, AGB, Galactic structure, Machine Learning, Stellar populations

Document Type

Dissertation

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