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Multi-faceted digital pyramid wavefront sensor

Author(s)
Akondi, Vyas  
Castillo, Sara  
Vohnsen, Brian  
Uri
http://hdl.handle.net/10197/13061
Date Issued
2014-07-15
Date Available
2022-08-15T16:19:17Z
Abstract
The modulated pyramid wavefront sensor is known for its high sensitivity and adjustable dynamic range. The need for mechanically moving parts in a modulated pyramid wavefront sensor can be overcome by using the recently proposed digital pyramid wavefront sensor. In this paper, a digital multi-faceted pyramid wavefront sensor is demonstrated with the use of a reflecting phase-only spatial light modulator. The four-pupil digital pyramid wavefront sensor with 4-facets is extended to 6 and 8-facets. It is noted from the experiments performed under identical low-noise conditions that the performance of the wavefront sensor in terms of the root mean square wavefront error remains nearly the same in cases of four, six and eight pupil configurations. Under the circumstances elucidated here, the results of simulations indicate that in the presence of scatter noise, the pyramid wavefront sensor with greater number of pupils could lead to an improvement over the standard four-pupil pyramid wavefront sensor. Noise from scattering makes the choice of optimal modulation radius critical while sensing in open-loop adaptive optics systems.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Elsevier
Journal
Optics Communications
Volume
323
Start Page
77
End Page
86
Copyright (Published Version)
2014 Elsevier
Subjects

Optics

Pyramid wavefront sen...

Wavefront sensing

Adaptive optics

Noise

Hartmann-Shack wavefr...

Spatial light modulat...

Adaptive optics

DOI
10.1016/j.optcom.2014.03.004
Language
English
Status of Item
Peer reviewed
ISSN
0030-4018
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Opt Commun Pyramid Sensor open access.pdf

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Checksum (MD5)

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Owning collection
Physics Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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