Abstract
ANGEL et al.1 recently showed how an artificial neural network could be used to measure optical phase distortion induced by atmospheric turbulence, and demonstrated by numerical simulation that such a system could be used to control the six 1.8-m mirrors of the Multiple Mirror Telescope by constantly adjusting them to compensate for atmospheric distortion of the image. The neural network estimates the phase distortion using two images of a reference star, or of a laser-produced guide star2, one image being at the best focus of the telescope while the other is intentionally out of focus. Here we report the successful test of a neural network with a real star. We applied a neural network to in- and out-of-focus images of Vega obtained with the 1.5-m single-mirror telescope at the Starfire Optical Range of the Air Force Phillips Laboratory near Albuquerque, New Mexico. The experimental results agree well with phase reconstructions obtained simultaneously with a conventional wave-front sensor.
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