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Disrupting Satellites and Dark Matter: Mapping the Milky Way's Hidden Structure
Disrupting Satellites and Dark Matter: Mapping the Milky Way's Hidden Structure
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103515
- ISBN
- 9798280748767
- DDC
- 530
- 서명/저자
- Disrupting Satellites and Dark Matter: Mapping the Milky Ways Hidden Structure
- 발행사항
- [Sl] : Princeton University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 195 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Lisanti, Mariangela.
- 학위논문주기
- Thesis (Ph.D.)--Princeton University, 2025.
- 초록/해제
- 요약The standard cosmological model, ΛCDM, successfully describes the large-scale structure of the universe but faces challenges at galactic scales, particularly in its predictions for low-mass, dark matter-dominated satellite galaxies undergoing tidal disruption. The tidal debris from these satellites, which form the bulk of galactic substructure, offer a critical testing ground for both baryonic and dark matter physics. This thesis utilizes theory, simulations, and observational data to advance our understanding of tidal debris populations and their implications for galaxy formation and dark matter models. To maximize the scientific potential of ongoing and future Galactic surveys, I develop a novel machine learning technique to infer missing stellar kinematic information. In early data releases from the Gaia mission, many stars lack line-of-sight velocity measurements, limiting their usefulness in kinematic studies. I design and implement a neural network that reconstructs these velocities, ultimately applying it to Gaia EDR3 to infer velocities for ∼ 92 million stars. This approach enables the identification and characterization of stars likely associated with the Milky Way's most significant recent merger, Gaia-Sausage-Enceladus. Next, I examine how the abundance and dynamics of populations of disrupting satellites are impacted by host galaxy properties by developing a novel, statistical approach. Using a suite of Milky Way-mass systems generated by a semi-analytic galaxy formation model, I show that variance across galaxies dominates the effects of specific host galaxy structural parameters on the abundance and dynamics of tidal debris. These findings highlight the need to account for host galaxy diversity in near-field cosmology, especially when assessing potential small-scale tensions with ΛCDM. Finally, I use a cosmological simulation to investigate how an LMC-like satellite perturbs the morphology and kinematics of stellar streams in a Milky Way-mass galaxy. By systematically separating the satellite's direct gravitational influence from its indirect effects on the host galaxy's evolving potential, I underscore the importance of accounting for significant satellite-induced perturbations and host halo responses when interpreting stellar stream observations. By harnessing theory, machine learning, simulations, and Galactic data, this thesis provides new insights into the nature of tidal debris and its role in refining our understanding of dark matter and galaxy evolution.
- 일반주제명
- Physics
- 일반주제명
- Astrophysics
- 일반주제명
- Quantum physics
- 일반주제명
- Astronomy
- 키워드
- Galaxies
- 키워드
- Machine learning
- 키워드
- Milky Way
- 키워드
- Stars
- 키워드
- Tidal disruption
- 기타저자
- Princeton University Physics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798280748767
■035 ▼a(MiAaPQ)AAI32038014
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aDropulic, Adriana.▼0(orcid)0000-0002-7352-6252
■24510▼aDisrupting Satellites and Dark Matter: Mapping the Milky Way's Hidden Structure
■260 ▼a[Sl]▼bPrinceton University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a195 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Lisanti, Mariangela.
■5021 ▼aThesis (Ph.D.)--Princeton University, 2025.
■520 ▼aThe standard cosmological model, ΛCDM, successfully describes the large-scale structure of the universe but faces challenges at galactic scales, particularly in its predictions for low-mass, dark matter-dominated satellite galaxies undergoing tidal disruption. The tidal debris from these satellites, which form the bulk of galactic substructure, offer a critical testing ground for both baryonic and dark matter physics. This thesis utilizes theory, simulations, and observational data to advance our understanding of tidal debris populations and their implications for galaxy formation and dark matter models. To maximize the scientific potential of ongoing and future Galactic surveys, I develop a novel machine learning technique to infer missing stellar kinematic information. In early data releases from the Gaia mission, many stars lack line-of-sight velocity measurements, limiting their usefulness in kinematic studies. I design and implement a neural network that reconstructs these velocities, ultimately applying it to Gaia EDR3 to infer velocities for ∼ 92 million stars. This approach enables the identification and characterization of stars likely associated with the Milky Way's most significant recent merger, Gaia-Sausage-Enceladus. Next, I examine how the abundance and dynamics of populations of disrupting satellites are impacted by host galaxy properties by developing a novel, statistical approach. Using a suite of Milky Way-mass systems generated by a semi-analytic galaxy formation model, I show that variance across galaxies dominates the effects of specific host galaxy structural parameters on the abundance and dynamics of tidal debris. These findings highlight the need to account for host galaxy diversity in near-field cosmology, especially when assessing potential small-scale tensions with ΛCDM. Finally, I use a cosmological simulation to investigate how an LMC-like satellite perturbs the morphology and kinematics of stellar streams in a Milky Way-mass galaxy. By systematically separating the satellite's direct gravitational influence from its indirect effects on the host galaxy's evolving potential, I underscore the importance of accounting for significant satellite-induced perturbations and host halo responses when interpreting stellar stream observations. By harnessing theory, machine learning, simulations, and Galactic data, this thesis provides new insights into the nature of tidal debris and its role in refining our understanding of dark matter and galaxy evolution.
■590 ▼aSchool code: 0181.
■650 4▼aPhysics
■650 4▼aAstrophysics
■650 4▼aQuantum physics
■650 4▼aAstronomy
■653 ▼aDark matter physics
■653 ▼aGalaxies
■653 ▼aMachine learning
■653 ▼aMilky Way
■653 ▼aStars
■653 ▼aTidal disruption
■690 ▼a0605
■690 ▼a0596
■690 ▼a0599
■690 ▼a0606
■71020▼aPrinceton University▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0181
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357458▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
