Physics & Astronomy ETDs

Publication Date

Spring 4-15-2024

Abstract

Galaxy-scale strong gravitational lenses offer a unique window into understanding the nature and distribution of dark matter at sub-galactic scales. Beyond the main-lens subhalos, the inclusion of line-of-sight dark matter halos has become essential in lens studies due to their substantial role in perturbing lensed images, making multi-plane lensing a crucial aspect of any lens study. We highlight that these line-of-sight halos appear extended tangentially along the lensing critical line in the effective convergence maps due to the nonlinear nature of multiplane lensing, resulting in a characteristic anisotropic signature in the deflection field. Leveraging tools from large-scale structure analyses, this anisotropic structure may be represented by a net quadrupole moment in the two-point correlation function of the effective convergence field. We emphasize that the monopole and quadrupole of this correlation function are sensitive indicators of the collisional properties of dark matter, offering insights into the presence of self-interacting dark matter in our Universe. In an era of growing integration of artificial intelligence in cosmology, we explore how a trained neural network can extract various two-point function multipoles directly from galaxy-galaxy strong lens images. This novel approach, coupled with forthcoming large-scale surveys, holds promise for enhancing our understanding of dark matter through strong gravitational lenses.

Degree Name

Physics

Level of Degree

Doctoral

Department Name

Physics & Astronomy

First Committee Member (Chair)

Francis-Yan Cyr-Racine

Second Committee Member

Dinesh Loomba

Third Committee Member

Rouzbeh Allahverdi

Fourth Committee Member

Annika H. G. Peter

Language

English

Document Type

Dissertation

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