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Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images

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Abstract

An 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 μm rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

© 1993 Optical Society of America

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