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

机译:使用人工神经网络从恒星图像中恢复大气相位畸变

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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. !13
机译:摘要:我们报道了一种新的实验验证方法,该方法可以直接从星光的聚焦图像直接确定大气相位。人工神经网络用于从两颗恒星图像中推断出相位,一个图像处于准确的聚焦位置,另一个图像有意地失焦。我们将网络应用于在新墨西哥州阿尔伯克基柯特兰空军基地的星火光学靶场(SOR)1.5 m望远镜上获得的维加图像。神经网络预测与使用常规Hartmann波前传感器的相位重建非常吻合。网络方法提供了一种简单,廉价的方法,可以在短期内在天文望远镜上实现自适应光学。 !13

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