Vector functions for direct analysis of annular wavefront slope data
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James C. Wyant College of Optical Sciences, University of ArizonaIssue Date
2022Keywords
Annular coefficientsAnnular vector functions
Annular wavefront
Optical fabrication
Wavefront analysis
Zernike coefficients
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Elsevier B.V.Citation
Mahajan, V. N., & Acosta, E. (2022). Vector functions for direct analysis of annular wavefront slope data. Results in Optics.Journal
Results in OpticsRights
Copyright © 2022 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
In the aberration analysis of a wavefront over a certain domain, the polynomials that are orthogonal over and represent balanced aberrations for this domain are used. For example, Zernike circle polynomials are used for analysis of a circular wavefront. When the data available for analysis are the slopes of a wavefront, the polynomials used are those that are orthogonal to the gradient of Zernike polynomials, and are irrotational. Similarly, the annular polynomials are used to analyze the annular wavefronts for systems with annular pupils, as in a rotationally symmetric two-mirror system. In this paper we derive the vector functions that are orthogonal to the gradients of annular polynomials and are irrotational so that they propagate minimum noise from the slope data to the annular aberration coefficients. These vector functions can be used directly to obtain independent annular aberration coefficients as their inner products with the slope data of annular wavefronts. The utility of the functions is demonstrated in a numerical simulation of noisy wavefront slope data to determine the annular coefficients. © 2022Note
Open access journalISSN
2666-9501Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1016/j.rio.2022.100216
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Except where otherwise noted, this item's license is described as Copyright © 2022 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).