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New Views of the Hot and Diffuse Gaseous Universe With the Thermal Sunyaev-Zel'dovich Effect
New Views of the Hot and Diffuse Gaseous Universe With the Thermal Sunyaev-Zel'dovich Effect
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153013
- ISBN
- 9798384045359
- DDC
- 523
- 서명/저자
- New Views of the Hot and Diffuse Gaseous Universe With the Thermal Sunyaev-Zeldovich Effect
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 150 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Bregman, Joel N.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약Most of the baryonic matter in the Universe is expected to exist as diffuse gas surrounding galaxies, galaxy groups, and galaxy clusters. Detecting this is a difficult task, especially when it is too diffuse to see in X-ray emission. The Sunyaev-Zel'dovich (SZ) effect offers an independent view of cosmological gas by measuring spectral distortions in the cosmic microwave background (CMB). In this thesis, I present the process of extracting SZ maps (y-maps) from Planck and WMAP data. These y-maps are used to estimate the gaseous contents around individual systems, particularly galaxy groups. Moreover, part of this work includes the first resolved SZ signal measured from a stack of nearby galaxy groups. Then I describe the efforts made improve the quality of current SZ data using machine learning. Specifically, I discuss two methodologies to extract the SZ signal with deep learning. Finally, I will conclude with a statement regarding the current state of SZ data and the notable caveats of using mock data and supervised learning to make real-world predictions.
- 일반주제명
- Astrophysics
- 일반주제명
- Astronomy
- 일반주제명
- Computer science
- 일반주제명
- Computational physics
- 키워드
- Machine learning
- 키워드
- Galaxy groups
- 키워드
- Deep learning
- 기타저자
- University of Michigan Astronomy and Astrophysics
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384045359
■035 ▼a(MiAaPQ)AAI31631491
■035 ▼a(MiAaPQ)umichrackham005611
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a523
■1001 ▼aPratt, Cameron T.
■24510▼aNew Views of the Hot and Diffuse Gaseous Universe With the Thermal Sunyaev-Zel'dovich Effect
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a150 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Bregman, Joel N.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aMost of the baryonic matter in the Universe is expected to exist as diffuse gas surrounding galaxies, galaxy groups, and galaxy clusters. Detecting this is a difficult task, especially when it is too diffuse to see in X-ray emission. The Sunyaev-Zel'dovich (SZ) effect offers an independent view of cosmological gas by measuring spectral distortions in the cosmic microwave background (CMB). In this thesis, I present the process of extracting SZ maps (y-maps) from Planck and WMAP data. These y-maps are used to estimate the gaseous contents around individual systems, particularly galaxy groups. Moreover, part of this work includes the first resolved SZ signal measured from a stack of nearby galaxy groups. Then I describe the efforts made improve the quality of current SZ data using machine learning. Specifically, I discuss two methodologies to extract the SZ signal with deep learning. Finally, I will conclude with a statement regarding the current state of SZ data and the notable caveats of using mock data and supervised learning to make real-world predictions.
■590 ▼aSchool code: 0127.
■650 4▼aAstrophysics
■650 4▼aAstronomy
■650 4▼aComputer science
■650 4▼aComputational physics
■653 ▼aMissing baryon problem
■653 ▼aSunyaev-Zel'dovich effect
■653 ▼aMachine learning
■653 ▼aGalaxy groups
■653 ▼aDeep learning
■690 ▼a0596
■690 ▼a0606
■690 ▼a0984
■690 ▼a0800
■690 ▼a0216
■71020▼aUniversity of Michigan▼bAstronomy and Astrophysics.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164531▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
