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All the Lenses: Large-Scale Hierarchical Inference of the Hubble Constant from Strong Gravitational Lenses with Bayesian Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	All the Lenses: Large-Scale Hierarchical Inference of the Hubble Constant from Strong Gravitational Lenses with Bayesian Deep Learning./
Author:	Park, Ji Won.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	125 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
Subject:	Stars & galaxies. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29176547
ISBN:	9798835549429

All the Lenses: Large-Scale Hierarchical Inference of the Hubble Constant from Strong Gravitational Lenses with Bayesian Deep Learning.
Park, Ji Won.

All the Lenses: Large-Scale Hierarchical Inference of the Hubble Constant from Strong Gravitational Lenses with Bayesian Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 125 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--Stanford University, 2022.

This item must not be sold to any third party vendors.

Unprecedented volumes of data from upcoming sky surveys will yield precise constraints on parameters governing the evolution history of the Universe. One that has received particular attention over the past decade is the Hubble constant (H0) describing the expansion rate of the Universe. This thesis focuses on measuring H0 from an astrophysical phenomenon called strong gravitational lensing.The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) will increase the sample size of strong lenses from ~100 to ~100,000. This creates an opportunity to obtain the most precise measurement of H0 to date. Fully realizing the potential of LSST data entails rapidly extracting cosmological information from the images, tables, and time series associated with these lenses. My research has focused on developing analysis techniques using Bayesian deep learning, which combines the efficiency of deep learning with principled uncertainty quantification. The techniques promise to automate the analysis of tens of thousands of strong lensing systems in a robust manner. They constitute core methodology that can combine information from all the LSST lenses -- with varying types and signal-to-noise ratios -- into a large-scale hierarchical inference of H0.

ISBN: 9798835549429Subjects--Topical Terms:

3683661
Stars & galaxies.

All the Lenses: Large-Scale Hierarchical Inference of the Hubble Constant from Strong Gravitational Lenses with Bayesian Deep Learning.
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500 $a Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
500 $a Advisor: Roodman, Aaron J. ;Burchat, P. ;Kahn, Steven M.
502 $a Thesis (Ph.D.)--Stanford University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Unprecedented volumes of data from upcoming sky surveys will yield precise constraints on parameters governing the evolution history of the Universe. One that has received particular attention over the past decade is the Hubble constant (H0) describing the expansion rate of the Universe. This thesis focuses on measuring H0 from an astrophysical phenomenon called strong gravitational lensing.The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) will increase the sample size of strong lenses from ~100 to ~100,000. This creates an opportunity to obtain the most precise measurement of H0 to date. Fully realizing the potential of LSST data entails rapidly extracting cosmological information from the images, tables, and time series associated with these lenses. My research has focused on developing analysis techniques using Bayesian deep learning, which combines the efficiency of deep learning with principled uncertainty quantification. The techniques promise to automate the analysis of tens of thousands of strong lensing systems in a robust manner. They constitute core methodology that can combine information from all the LSST lenses -- with varying types and signal-to-noise ratios -- into a large-scale hierarchical inference of H0.
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