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Proceedings Paper

Recovery of atmospheric phase distortion from stellar images using an artificial neural network
Author(s): David G. Sandler; Todd K. Barrett; Robert Q. Fugate
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Paper Abstract

We report recent experimental verification of an new method to determine atmospheric phase directly from focused images of starlight. An artificial neural network is used to infer the phase from two images of a star, one at the exact focus and another intentionally out of focus. We applied the network to images of Vega obtained on the 1.5 m telescope at Starfire Optical Range (SOR), Kirtland Air Force Base, Albuquerque, New Mexico. Neural network predictions agree well with phase reconstructions using a conventional Hartmann wavefront sensor. The network approach offers a simple, inexpensive way to implement adaptive optics on astronomical telescopes in the near term.

Paper Details

Date Published: 13 January 1992
PDF: 9 pages
Proc. SPIE 1543, Active and Adaptive Optical Components, (13 January 1992); doi: 10.1117/12.51204
Show Author Affiliations
David G. Sandler, Thermo Electron Technologies Corp. (United States)
Todd K. Barrett, Thermo Electron Technologies Corp. (United States)
Robert Q. Fugate, Phillips Lab. (United States)


Published in SPIE Proceedings Vol. 1543:
Active and Adaptive Optical Components
Mark A. Ealey, Editor(s)

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