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

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

Jan 1992

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1992spie.1543..491s&link_type=abstract

Proc. SPIE Vol. 1543, p. 491-499, Active and Adaptive Optical Components, Mark A. Ealey; Ed.

Physics

Optics

1

Scientific paper

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.

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