Recovery of atmospheric phase distortion from stellar images using an artificial neural network

David G. Sandler; Todd K. Barrett; Robert Q. Fugate

doi:10.1117/12.51204

13 January 1992 Recovery of atmospheric phase distortion from stellar images using an artificial neural network

David G. Sandler, Todd K. Barrett, Robert Q. Fugate

Proceedings Volume 1543, Active and Adaptive Optical Components; (1992) https://doi.org/10.1117/12.51204
Event: San Diego, '91, 1991, San Diego, CA, United States

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.

Citation Download Citation

David G. Sandler, Todd K. Barrett, and 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); https://doi.org/10.1117/12.51204

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