13 January 1992 Recovery of atmospheric phase distortion from stellar images using an artificial neural network
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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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David G. Sandler, Todd K. Barrett, Robert Q. Fugate, "Recovery of atmospheric phase distortion from stellar images using an artificial neural network", Proc. SPIE 1543, Active and Adaptive Optical Components, (13 January 1992); doi: 10.1117/12.51204; https://doi.org/10.1117/12.51204
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