Machine learning based wavefront estimation for the Rubin observatory
Abstract/Contents
- Abstract
- We present a new conceptual framework for wavefront sensing for wide-field telescopes. The framework divides the problem into two subproblems that are highly amenable to machine learning and optimization. The first involves making local wavefront estimates with a convolutional neural network. The second involves interpolating the optics wavefront from all the local estimates with least squares. In this thesis, we develop simulated observations and images from the upcoming Rubin Observatory to develop, refine and assess this new algorithm. In a realistic Rubin mini-survey, the algorithm reduces the total magnitude of the optics wavefront by 66%, the optics PSF FWHM by 27%, and increases the Strehl ratio by a factor of 6. Ultimately, this algorithm has the potential to improve image quality on Rubin and multiple current and upcoming wide-field telescopes, and to boost the scientific returns for astrophysics and cosmology.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Thomas, David Rees |
|---|---|
| Degree supervisor | Kahn, Steven M. (Steven Michael) |
| Thesis advisor | Kahn, Steven M. (Steven Michael) |
| Thesis advisor | Boyd, Stephen P |
| Thesis advisor | Burchat, P. (Patricia) |
| Degree committee member | Boyd, Stephen P |
| Degree committee member | Burchat, P. (Patricia) |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | David Thomas. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kz266yd9066 |
Access conditions
- Copyright
- © 2021 by David Rees Thomas
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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