Author ORCID Identifier

https://orcid.org/0009-0006-5300-2976

Date of Award

Summer 8-6-2025

Document Type

Thesis (Ph.D.)

Department or Program

Physics and Astronomy

First Advisor

Brian Chaboyer

Abstract

Stellar evolution models serve as fundamental tools in astronomy, essential for interpreting the interiors and evolutionary stages of stars, and have wide applications in astronomy, from searching for exoplanets to estimating the age of the universe. This thesis explores the inherent uncertainties arising from the complex physics used in stellar evolution models. By adopting a Monte Carlo approach to vary over 20 key stellar evolution parameters, we quantify their impact on model predictions, highlighting substantial influences on age, luminosity, and temperature. Through a detailed analysis of Milky Way globular clusters (GCs), this work rigorously investigates the uncertainties inherent in stellar models. Using high-precision photometric data from the Hubble Space Telescope Advanced Camera for Surveys, we apply innovative statistical methods to fully utilize information contained in the color-magnitude diagram to achieve robust and precise absolute age determinations. Our results, validated through comparisons with detached eclipsing binaries and calibration stars, yield a pioneering absolute age-metallicity relation for Galactic GCs, consistent with cosmological age constraints from Planck observations. Recognizing the limitations posed by traditional grid-based stellar evolution databases, this thesis also introduces the Dartmouth Stellar Evolution Emulator (DSEE). Leveraging advanced machine learning techniques, particularly normalizing flows, DSEE efficiently emulates continuous stellar evolution models trained on an unprecedentedly extensive and comprehensive dataset comprising millions of evolved stellar models. This emulator achieves rapid, high-precision interpolation and extrapolation of stellar evolutionary models across extensive parameter spaces, setting a new unified benchmark for stellar evolution models. Ultimately, this work advances our capacity to exploit large observational datasets, with potential for transformative applications in stellar populations, exoplanet characterization, galaxy evolution, and cosmology.

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