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arXiv 2005.14078astro-ph.IM
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开环自适应光学的人工神经网络波前预测

Wavefront prediction using artificial neural networks for open-loop Adaptive Optics

Xuewen Liu, Tim Morris, Chris Saunter, Francisco Javier de Cos Juez, Carlos González-Gutiérrez, Lisa Bardou

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摘要：

自适应光学（AO）系统的控制回路中的延迟会严重限制性能。在冻结流假设下，线性预测控制技术可以克服这一点，然而，识别和跟踪相关的湍流参数（如风速）是需要这样的参数技术。这可能使实际实现复杂化，并在遇到可变条件时引入稳定性问题。在这里，我们提出了一个非线性波前预测器使用长短期记忆（LSTM）人工神经网络（ANN），假设没有先验知识的大气，因此不需要用户输入。人工神经网络的目的是预测开环波前斜率测量的夏克哈特曼波前传感器（SH-WFS）提前一帧，以补偿单帧延迟在一个模拟的$7\times7$单共轭自适应光学（SCAO）系统工作在150 Hz。我们描述了LSTM ANN的训练机制如何影响预测性能，并展示了预测器的性能如何在不同的导航星星等下变化。我们发现，当风速和风向都变化时，预测保持稳定。然后，我们将我们的方法扩展到一个更现实的两帧延迟系统。与$78.3\pm4.4$ nm RMS的带宽误差相比，在相同条件下操作的无延迟系统的预测误差在19.9至40.0 nm RMS内的所有模拟条件下，使用LSTM预测器时的AO系统性能都得到了增强。

英文摘要：

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 nonlinear 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 $7\times7$ single-conjugate adaptive optics (SCAO) 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 to 40.0 nm RMS of a latency-free system operating under the same conditions compared to a bandwidth error of $78.3\pm4.4$ nm RMS.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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