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Weak Lensing Cosmology and Its Astrophysical Systematics Through Machine Learning- [electronic resource]
Weak Lensing Cosmology and Its Astrophysical Systematics Through Machine Learning- [electronic resource]
상세정보
- 자료유형
- 학위논문
- Control Number
- 0016933148
- International Standard Book Number
- 9798379780456
- Dewey Decimal Classification Number
- 520
- Main Entry-Personal Name
- Lu, Tianhuan.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Columbia University., 2023
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- Physical Description
- 1 online resource(177 p.)
- General Note
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- General Note
- Advisor: Haiman, Zoltan.
- Dissertation Note
- Thesis (Ph.D.)--Columbia University, 2023.
- Restrictions on Access Note
- This item must not be sold to any third party vendors.
- Summary, Etc.
- 요약In this dissertation, we investigate weak lensing cosmology and its astrophysical systematics by employing machine learning techniques. We focus on addressing the discrepancy between two previous weak lensing analyses on CFHTLenS data, understanding the impact of baryons on weak lensing statistics, and leveraging convolutional neural networks (CNNs) for constraining cosmological and baryonic parameters.First, we perform a side-by-side comparison of the two-point correlation function and power spectrum analyses on CFHTLenS data, identifying excess power in the data on small scales and discussing potential origins of this excess power. Next, we study the effect of baryons on weak lensing statistics using the baryonic correction model, demonstrating that marginalizing over baryonic parameters will degrade constraints in the Ωm-\uD835\uDF0E8 parameter space, but the degradation can be mitigated by combining the lensing power spectrum and peak counts.Second, we explore the use of CNNs to constrain cosmological and baryonic parameters. We find that CNNs can achieve tighter constraints in Ωm-\uD835\uDF0E8 space than traditional methods on simulation data. We then apply our pipeline to the HSC first-year weak lensing shear catalog. We find that statistical uncertainties of the parameters by the CNNs are smaller than those from the power spectrum and peak counts, showing that CNNs can extract additional cosmological information from weak lensing data even in a real experiment.
- Subject Added Entry-Topical Term
- Astronomy.
- Subject Added Entry-Topical Term
- Astrophysics.
- Index Term-Uncontrolled
- Cosmology
- Index Term-Uncontrolled
- Convolutional neural networks
- Index Term-Uncontrolled
- Baryonic parameters
- Index Term-Uncontrolled
- Power spectrum
- Index Term-Uncontrolled
- Machine learning
- Added Entry-Corporate Name
- Columbia University Astronomy
- Host Item Entry
- Dissertations Abstracts International. 85-01B.
- Host Item Entry
- Dissertation Abstract International
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:642228
MARC
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■020 ▼a9798379780456
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a520
■1001 ▼aLu, Tianhuan.
■24510▼aWeak Lensing Cosmology and Its Astrophysical Systematics Through Machine Learning▼h[electronic resource]
■260 ▼a[S.l.]▼bColumbia University. ▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a1 online resource(177 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Haiman, Zoltan.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aIn this dissertation, we investigate weak lensing cosmology and its astrophysical systematics by employing machine learning techniques. We focus on addressing the discrepancy between two previous weak lensing analyses on CFHTLenS data, understanding the impact of baryons on weak lensing statistics, and leveraging convolutional neural networks (CNNs) for constraining cosmological and baryonic parameters.First, we perform a side-by-side comparison of the two-point correlation function and power spectrum analyses on CFHTLenS data, identifying excess power in the data on small scales and discussing potential origins of this excess power. Next, we study the effect of baryons on weak lensing statistics using the baryonic correction model, demonstrating that marginalizing over baryonic parameters will degrade constraints in the Ωm-\uD835\uDF0E8 parameter space, but the degradation can be mitigated by combining the lensing power spectrum and peak counts.Second, we explore the use of CNNs to constrain cosmological and baryonic parameters. We find that CNNs can achieve tighter constraints in Ωm-\uD835\uDF0E8 space than traditional methods on simulation data. We then apply our pipeline to the HSC first-year weak lensing shear catalog. We find that statistical uncertainties of the parameters by the CNNs are smaller than those from the power spectrum and peak counts, showing that CNNs can extract additional cosmological information from weak lensing data even in a real experiment.
■590 ▼aSchool code: 0054.
■650 4▼aAstronomy.
■650 4▼aAstrophysics.
■653 ▼aCosmology
■653 ▼aConvolutional neural networks
■653 ▼aBaryonic parameters
■653 ▼aPower spectrum
■653 ▼aMachine learning
■690 ▼a0606
■690 ▼a0596
■71020▼aColumbia University▼bAstronomy.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933148▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
