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Photometric Redshift and Ellipticity Measurements for Cosmology With Probabilistic Neural Networks
Photometric Redshift and Ellipticity Measurements for Cosmology With Probabilistic Neural Networks
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152040
- ISBN
- 9798383599969
- DDC
- 523
- 저자명
- Jones, Evan.
- 서명/저자
- Photometric Redshift and Ellipticity Measurements for Cosmology With Probabilistic Neural Networks
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 176 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Do, Tuan H.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Cosmological weak lensing probes can inform us of the contents and evolution of the universe, including the properties of dark matter and dark energy, which collectively make up ∼ 95% of the universe. We live in an exciting period in scientific history; large scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) will soon provide imaging for over a billion celestial objects, which timely coincides with recent advancements in probabilistic image-based machine learning. It is incumbent on scientists to leverage recent advancements to extract as much information as possible from large scale astronomical surveys to probe our universe. This thesis contains my contribution toward this objective.Precision cosmological measurements require accurate data analysis with precise uncertainties. The two critical data analysis tasks for weak lensing cosmological probes are 1) photometric redshift (photo-z) estimation and 2) galaxy shear estimation. These quantities allow us to map the distribution of galaxies in the sky and quantify the distribution of dark matter. Here we present results for photo-z estimation and galaxy shape estimation using probabilistic neural networks, using a novel dataset derived from the Hyper Suprime-Cam (HSC) Survey.In Chapter 1, we provide an introduction to weak lensing cosmological probes, photo-z estimation, and shear estimation. In Chapter 2, we introduce the machine-learning-ready dataset derived from HSC consisting of galaxy photometry, galaxy images, and spectroscopic redshifts. We make this dataset publicly available and utilize it for all photo-z estimation analyses in this work. In Chapter 3, we present a probabilistic photo-z estimation model using a Bayesian neural network (BNN) and compare its performance to alternative methods. In Chapter 4, we present an image-based probabilistic photo-z estimation model using a Bayesian convolutional neural network (BCNN) and compare its performance to alternative methods. In Chapter 5, we present an image-based probabilistic model for galaxy ellipticity estimation (as a proxy for shear estimation) evaluated on HSC galaxy images using a custom BCNN. In the Appendix we provide a roadmap by which one can utilize the photo-z and potential shear estimation models in this thesis to perform a weak lensing measurement.
- 일반주제명
- Astrophysics
- 일반주제명
- Astronomy
- 일반주제명
- Computational physics
- 키워드
- Cosmology
- 키워드
- Dark energy
- 키워드
- Dark matter
- 키워드
- Machine learning
- 키워드
- Redshift
- 키워드
- Shear
- 기타저자
- University of California, Los Angeles Astronomy and Astrophysics 00EB
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017162678
■00520250211152040
■006m o d
■007cr#unu||||||||
■020 ▼a9798383599969
■035 ▼a(MiAaPQ)AAI31336608
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a523
■1001 ▼aJones, Evan.
■24510▼aPhotometric Redshift and Ellipticity Measurements for Cosmology With Probabilistic Neural Networks
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a176 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Do, Tuan H.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aCosmological weak lensing probes can inform us of the contents and evolution of the universe, including the properties of dark matter and dark energy, which collectively make up ∼ 95% of the universe. We live in an exciting period in scientific history; large scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) will soon provide imaging for over a billion celestial objects, which timely coincides with recent advancements in probabilistic image-based machine learning. It is incumbent on scientists to leverage recent advancements to extract as much information as possible from large scale astronomical surveys to probe our universe. This thesis contains my contribution toward this objective.Precision cosmological measurements require accurate data analysis with precise uncertainties. The two critical data analysis tasks for weak lensing cosmological probes are 1) photometric redshift (photo-z) estimation and 2) galaxy shear estimation. These quantities allow us to map the distribution of galaxies in the sky and quantify the distribution of dark matter. Here we present results for photo-z estimation and galaxy shape estimation using probabilistic neural networks, using a novel dataset derived from the Hyper Suprime-Cam (HSC) Survey.In Chapter 1, we provide an introduction to weak lensing cosmological probes, photo-z estimation, and shear estimation. In Chapter 2, we introduce the machine-learning-ready dataset derived from HSC consisting of galaxy photometry, galaxy images, and spectroscopic redshifts. We make this dataset publicly available and utilize it for all photo-z estimation analyses in this work. In Chapter 3, we present a probabilistic photo-z estimation model using a Bayesian neural network (BNN) and compare its performance to alternative methods. In Chapter 4, we present an image-based probabilistic photo-z estimation model using a Bayesian convolutional neural network (BCNN) and compare its performance to alternative methods. In Chapter 5, we present an image-based probabilistic model for galaxy ellipticity estimation (as a proxy for shear estimation) evaluated on HSC galaxy images using a custom BCNN. In the Appendix we provide a roadmap by which one can utilize the photo-z and potential shear estimation models in this thesis to perform a weak lensing measurement.
■590 ▼aSchool code: 0031.
■650 4▼aAstrophysics
■650 4▼aAstronomy
■650 4▼aComputational physics
■653 ▼aCosmology
■653 ▼aDark energy
■653 ▼aDark matter
■653 ▼aMachine learning
■653 ▼aRedshift
■653 ▼aShear
■690 ▼a0596
■690 ▼a0606
■690 ▼a0800
■690 ▼a0216
■71020▼aUniversity of California, Los Angeles▼bAstronomy and Astrophysics 00EB.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162678▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
