자료유형  

 학위논문

Control Number  

0017162678

International Standard Book Number  

9798383599969

Dewey Decimal Classification Number  

523

Main Entry-Personal Name  

Jones, Evan.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of California, Los Angeles., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

176 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

General Note  

Advisor: Do, Tuan H.

Dissertation Note  

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Summary, Etc.  

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.

Subject Added Entry-Topical Term  

Astrophysics.

Subject Added Entry-Topical Term  

Astronomy.

Subject Added Entry-Topical Term  

Computational physics.

Index Term-Uncontrolled  

Cosmology

Index Term-Uncontrolled  

Dark energy

Index Term-Uncontrolled  

Dark matter

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Redshift

Index Term-Uncontrolled  

Shear

Added Entry-Corporate Name  

University of California, Los Angeles Astronomy and Astrophysics 00EB

Host Item Entry  

Dissertations Abstracts International. 86-02B.

Electronic Location and Access  

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Control Number  

joongbu:658170