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Artificial neural network for the determination of Hubble Space Telescope aberration from stellar imagesAn artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 micron rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.
Document ID
Document Type
Reprint (Version printed in journal)
Barrett, Todd K.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Sandler, David G.
(Thermo Electron Technologies San Diego, CA, United States)
Date Acquired
August 16, 2013
Publication Date
April 1, 1993
Publication Information
Publication: Applied Optics
Volume: 32
Issue: 10
ISSN: 0003-6935
Subject Category
Accession Number
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