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Machine Learning Based Wavefront Estimation for the Rubin Observatory.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Based Wavefront Estimation for the Rubin Observatory./
作者:	Thomas, David Rees.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	93 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Telescopes. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29003882
ISBN:	9798209788331

Machine Learning Based Wavefront Estimation for the Rubin Observatory.
Thomas, David Rees.

Machine Learning Based Wavefront Estimation for the Rubin Observatory. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 93 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

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 by minimizing a convex loss function. In this thesis, we develop simulated observations and images from the upcoming Rubin Observatory to develop, refine, and assess this new algorithm. Much of this work is also summarized in [1] and [2], although here we describe it in more detail.Our unique contributions are as follows. We isolated wavefront sensing problem and decomposed it into two sub-problems. We created benchmark datasets for both sub-problems. We demonstrated a convolutional neural network can predict local wavefront from donut images. We showed that the global wavefront can be interpolated with least squares from the local wavefront estimates. Finally, we developed a wavefront control simulation environment and used it to assess three canonical control strategies.The algorithm has great practical properties - it is transparent, robust, low latency, and high bandwidth - and achieves stunning performance. In a realistic Rubin minisurvey, 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. The resulting sharper images have the potential to boost the scientific payload for astrophysics and cosmology.

ISBN: 9798209788331Subjects--Topical Terms:

539024
Telescopes.

Machine Learning Based Wavefront Estimation for the Rubin Observatory.
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520 $a 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 by minimizing a convex loss function. In this thesis, we develop simulated observations and images from the upcoming Rubin Observatory to develop, refine, and assess this new algorithm. Much of this work is also summarized in [1] and [2], although here we describe it in more detail.Our unique contributions are as follows. We isolated wavefront sensing problem and decomposed it into two sub-problems. We created benchmark datasets for both sub-problems. We demonstrated a convolutional neural network can predict local wavefront from donut images. We showed that the global wavefront can be interpolated with least squares from the local wavefront estimates. Finally, we developed a wavefront control simulation environment and used it to assess three canonical control strategies.The algorithm has great practical properties - it is transparent, robust, low latency, and high bandwidth - and achieves stunning performance. In a realistic Rubin minisurvey, 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. The resulting sharper images have the potential to boost the scientific payload for astrophysics and cosmology.
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