Xuewen Liu
Wavefront prediction using artificial neural networks for open-loop Adaptive Optics
Liu, Xuewen; Morris, Tim; Saunter, Chris; Juez, Francisco Javier de Cos; Gonzalez-Gutierrez, Carlos; Bardou, Lisa
Authors
Tim Morris
Chris Saunter
Francisco Javier de Cos Juez
Carlos Gonzalez-Gutierrez
Lisa Bardou
Abstract
Latency in the control loop of adaptive optics (AO) systems can severely limit performance. Under the frozen flow hypothesis linear predictive control techniques can overcome this; however, identification and tracking of relevant turbulent parameters (such as wind speeds) is required for such parametric techniques. This can complicate practical implementations and introduce stability issues when encountering variable conditions. Here, we present a non-linear wavefront predictor using a long short-term memory (LSTM) artificial neural network (ANN) that assumes no prior knowledge of the atmosphere and thus requires no user input. The ANN is designed to predict the open-loop wavefront slope measurements of a Shack–Hartmann wavefront sensor (SH-WFS) one frame in advance to compensate for a single-frame delay in a simulated 7 × 7 single-conjugate adaptive optics system operating at 150 Hz. We describe how the training regime of the LSTM ANN affects prediction performance and show how the performance of the predictor varies under various guide star magnitudes. We show that the prediction remains stable when both wind speed and direction are varying. We then extend our approach to a more realistic two-frame latency system. AO system performance when using the LSTM predictor is enhanced for all simulated conditions with prediction errors within 19.9–40.0 nm RMS of a latency-free system operating under the same conditions compared to a bandwidth error of 78.3 ± 4.4 nm RMS.
Citation
Liu, X., Morris, T., Saunter, C., Juez, F. J. D. C., Gonzalez-Gutierrez, C., & Bardou, L. (2020). Wavefront prediction using artificial neural networks for open-loop Adaptive Optics. Monthly Notices of the Royal Astronomical Society, 496(1), 456-464. https://doi.org/10.1093/mnras/staa1558
Journal Article Type | Article |
---|---|
Acceptance Date | May 29, 2020 |
Online Publication Date | Jun 4, 2020 |
Publication Date | 2020-07 |
Deposit Date | Jun 4, 2020 |
Publicly Available Date | Jun 18, 2020 |
Journal | Monthly Notices of the Royal Astronomical Society |
Print ISSN | 0035-8711 |
Electronic ISSN | 1365-2966 |
Publisher | Royal Astronomical Society |
Peer Reviewed | Peer Reviewed |
Volume | 496 |
Issue | 1 |
Pages | 456-464 |
DOI | https://doi.org/10.1093/mnras/staa1558 |
Related Public URLs | https://arxiv.org/abs/2005.14078 |
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Copyright Statement
This article has been accepted for publication in Monthly notices of the Royal Astronomical Society. ©: 2020 The Author(s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
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