A Neural Network Architecture for Reconstruction of Turbulence Degraded Point Spread Functions

Astronomical images, degraded by the effects of atmospheric turbulence, can be corrected in real-time using adaptive optics (AO). A reconstructed wavefront, measured using a reference object with a small angular separation from a target object, is used to compensate aberrations effecting a target object. The conjugate of the recovered wavefront is used to alter the optical path of a telescope for correction. To ensure high performance, a constraint is imposed on the angular separation between a reference object, such as a bright guide star, and target object which is typically fainter. This constraint is referred to as the isoplanatic angle, θ0. Given the sparsity of natural guide stars, combined with limitations on the use of artificial guide stars, severe restrictions are imposed on the field of view (FOV) and turbulence compensation over the anisoplanatic region. Our aim is to use aberration data from two or more reference objects to extend the isoplantatic angle. This paper outlines a system architecture and proposes the use of artificial neural networks (ANN) to provide extended FOV coverage for real-time astronomical image restoration