자료유형  

 학위논문

Control Number  

0017161816

International Standard Book Number  

9798382777269

Dewey Decimal Classification Number  

530

Main Entry-Personal Name  

Tsang, Arthur Leonard.

Publication, Distribution, etc. (Imprint  

[S.l.] : Harvard University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

171 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Dvorkin, Cora.

Dissertation Note  

Thesis (Ph.D.)--Harvard University, 2024.

Summary, Etc.  

요약Observations of the Universe generally support ΛCDM, but since distant structures ≲ 109 \uD835\uDC40⊙ are usually too dim to observe, their properties are still poorly constrained. Competing particle models of Dark Matter (DM) make different predictions, so these scales can teach us about the nature of DM, even in the absence of a direct detection. A promising probe is to use the subtle gravitational lensing effect of these dim structures, which is directly sensitive to mass rather than luminosity. This effect is too weak to measure on its own, but a strong gravitational lens, where one background object is lensed and appears (distorted) in multiple places, provides enough additional redundancy that the different copies of the background object can be compared and analyzed for weak gravitational perturbations.This thesis consists of three parts. (1) We study the combined statistical effect of many small gravitational perturbers. These come in two varieties, subhalos and interlopers. Subhalos physically lie within the main lens (a galactic halo), whereas interlopers are aligned by chance along the line of sight. The statistical effect due to subhalos has been studied before, but we show that interlopers are most likely the dominant component and certainly cannot be ignored in future analyses. (2) We analyze a real observation of a strong lens known to harbor a relatively large, individually detectable perturber. We reanalyze the system and find the perturber is better fit as a line-of-sight interloper rather than a subhalo as initially assumed. (3) We demonstrate how machine learning can accelerate the processing required to find individual perturbers. This is pertinent because we expect of order 105 strong lenses discovered by 2030, and traditional sampling-based methods are too computationally expensive to scale to this influx of data. We demonstrate that a UNet can find individual subhalos in realistic simulated lenses, but the substructure must have a high concentration parameter.

Subject Added Entry-Topical Term  

Physics.

Subject Added Entry-Topical Term  

Astrophysics.

Subject Added Entry-Topical Term  

Statistical physics.

Subject Added Entry-Topical Term  

Computational physics.

Index Term-Uncontrolled  

Cosmology

Index Term-Uncontrolled  

Dark matter

Index Term-Uncontrolled  

Gravitational lensing

Index Term-Uncontrolled  

Machine learning

Index Term-Uncontrolled  

Line-of-sight

Added Entry-Corporate Name  

Harvard University Physics

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:656824