Measuring the unseen universe with statistical vision : strong lensing as a probe of small-scale structure
Abstract/Contents
- Abstract
- For decades, modern cosmology has held that the majority of the matter in our Universe is cold, collisionless dark matter (CDM). Many of our dark matter theories impose scales at which the CDM paradigm breaks down, mainly by changing the distribution of collapsed structures (halos) at low masses. An investigative priority of modern astrophysics has been searching for these violations of CDM predictions. Probing dark matter at these scales is challenging; dark matter halos are traditionally traced by the galaxies they host, but at low masses, we do not fully understand the connection between halos and galaxies. However, strong gravitational lenses are sensitive to low-mass halos even if they host no galaxies. In this thesis, I develop the statistical tools that allow us to use strong lenses to measure the small-scale, dark matter structure underlying our Universe. I present work that leverages neural networks to produce posterior distributions for the parameters underlying strong gravitational lensing images. This work includes the development of a hierarchical inference framework that corrects for the implicit prior encoded into the network by the training distribution. After showing that we can use the technique to constrain the population statistics of lenses without low-mass halos, I present work that extends the methodology to measurements of the subhalo mass function (SHMF). With the aid of new simulation tools, the results demonstrate that we can reliably infer the SHMF across disparate configurations of hundreds of lenses. I then discuss the improvements that can be made as the methodology is extended to the data. I conclude by outlining the physics that can be measured with this new set of tools. I argue that the advances in this thesis serve as a foundation for turning strong gravitational lenses into a sensitive probe of dark matter physics.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Wagner-Carena, Sebastian Matthias |
|---|---|
| Degree supervisor | Wechsler, Risa H. (Risa Heyrman) |
| Thesis advisor | Wechsler, Risa H. (Risa Heyrman) |
| Thesis advisor | Marshall, Phil |
| Thesis advisor | Roodman, Aaron J. (Aaron Jay), 1964- |
| Degree committee member | Marshall, Phil |
| Degree committee member | Roodman, Aaron J. (Aaron Jay), 1964- |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Physics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sebastian M. Wagner-Carena. |
|---|---|
| Note | Submitted to the Department of Physics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/pb604fs0451 |
Access conditions
- Copyright
- © 2023 by Sebastian Matthias Wagner-Carena
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