Understanding the Milky Way with Stars

"How do galaxies form?" is one of the most important questions in modern astrophysics. Hierarchical growth, the most plausible theory behind galaxy formation, suggests that galaxies, including the Milky Way, assemble through the accretion of smaller systems, over a scaffolding of the invisible Dark Matter. Such growth is evidenced by the differences in stellar structures found in the Galaxy over the last few decades, accelerated most recently by the Gaia space mission. Yet, we still lack a full picture of the formation of the Milky Way and its stellar components, and we are even further in understanding its underlying Dark Matter distribution. For the latter, discrepancies between observations and predictions from CDM model at galactic scales have sparked debate about how well this model accounts for the evolution of the Milky Way. Stellar tracers provide a powerful tool for examining these discrepancies, helping us explore the hierarchical assembly of galaxies in the Local Group and test different models for dark matter. At the same time, cosmological simulations and machine learning techniques offer a bridge between the theory and observations. In this thesis, I combine observation of stellar kinematics and chemistry with cosmological simulations to understand the formation and evolution of the Milky Way and its satellite dwarf galaxies. I map the dark matter distributions in the Milky Way and one of its ultra-faint dwarf galaxies using stellar dynamics, combining simulations of tidal disruption with observational data to study ongoing merger events and how hierarchical assembly shaped the Milky Way today. I conduct robust machine learning searches of kinematic substructures from disrupted dwarf galaxy debris in the Milky Way and utilize stellar heavy element abundances to probe the galaxies that merged with the Milky Way in the past. Lastly, I develop synthetic surveys from simulations to bridge gaps between theory and observation, testing the robustness of current and future methodologies in understanding how the Milky Way came to be.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Physics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Ou, Xiaowei

Advisors dc:contributor.advisor

• Frebel, Anna
• Necib, Lina

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164126

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164126