Improving inference of cosmological parameters with advanced statistical techniques using simulations
Abstract/Contents
- Abstract
- The focus of this thesis is the improvement of cosmological inferences through the use of advanced statistical techniques applied to simulations. This thesis approaches the challenge of optimal cosmological inference through two main approaches. The first approach is my work with cosmological emulators as part of the Aemulus collaboration. I first describe the Aemulus simulations and emulators for the halo mass function and redshift space galaxy clustering that I contributed to. Next, I describe analyses using Aemulus to study the effect of galaxy secondary bias on cosmological inferences. This includes descriptions of new models for galaxy occupation, and analyses of their ability to describe realistic galaxy distributions, as well as the use of those models to build emulators for mock analysis. Finally, I discuss work using an emulator for a new statistics, kNN-CDFs, to measure galaxy secondary bias and cosmological parameters. The second approach is my work studying cosmological neural networks. I describe new techniques I've developed to identify the features identified by cosmological neural networks in weak lensing fields.
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | McLaughlin, Sean William |
|---|---|
| Degree supervisor | Wechsler, Risa H. (Risa Heyrman) |
| Thesis advisor | Wechsler, Risa H. (Risa Heyrman) |
| Thesis advisor | Abel, Tom |
| Thesis advisor | Burchat, P. (Patricia) |
| Degree committee member | Abel, Tom |
| Degree committee member | Burchat, P. (Patricia) |
| Associated with | Stanford University, Department of Physics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sean McLaughlin. |
|---|---|
| Note | Submitted to the Department of Physics. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Sean William McLaughlin
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