Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images

Abstract

An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 microm rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

Phase-retrieval analysis of pre- and post-repair Hubble Space Telescope images

“…It is possible, however, that when they fitted their single-plane data they encountered a local minimum, given the agreement between the multiple-plane fitting results and our single-plane ones. Other phase-retrieval studies 3,7,8 that employed single-plane models have reported the same value as ours. We are convinced by the consistency and agreement in our results that single-plane diffraction models are sufficient for the use in the HST phase-retrieval, optical-studies, and deconvolution.…”

Artificial intelligence-assisted light control and computational imaging through scattering media

“…Applications of arti¯cial neural networks (ANN) are seen in this¯eld to estimate the wavefront distortions in AO system. [64][65][66] These works, however, will not be discussed here; instead, we will focus on the recent developments that are related to direct wavefront control. In AO implementations, the distorted wavefronts are typically measured by a Shack-Hartmann (SH) wavefront sensor, with which the slopes of each SH lenslet are recovered.…”

An Improved Method of Measuring Wavefront Aberration Based on Image with Machine Learning in Free Space Optical Communication

“…Owing to the high fitting ability and successful application of machine learning in other fields, some research on measuring wavefront aberration with machine learning has been completed. A back propagation (BP) neural network was used to measure wavefront aberration and was verified on the Hubble telescope [8,9,10]. The input to the network is a one-dimensional vector which is composed by all pixels of the point spread functions (PSFs) in the focal and defocus planes.…”