Adaptive optics (AO) has become an indispensable tool for ground-based telescopes to mitigate atmospheric seeing and obtain high angular resolution observations. Predictive control aims to overcome latency in AO systems: the inevitable time delay between wavefront measurement and correction. A current method of predictive control uses the empirical orthogonal functions (EOFs) framework borrowed from weather prediction, but the advent of modern machine learning and the rise of neural networks (NNs) offer scope for further improvement. Here, we evaluate the potential application of NNs to predictive control and highlight the advantages that they offer. We first show their superior regularization over the standard truncation regularization used by the linear EOF method with on-sky data before demonstrating the NNs’ capacity to model nonlinearities on simulated data. This is highly relevant to the operation of pyramid wavefront sensors (PyWFSs), as the handling of nonlinearities would enable a PyWFS to be used with low modulation and deliver extremely sensitive wavefront measurements.

中文翻译：

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使用神经网络的自适应光学的预测控制

自适应光学（AO）已成为地基望远镜必不可少的工具，以减轻大气中的视线并获得高角度分辨率的观测结果。预测控制旨在克服AO系统中的延迟：波前测量和校正之间不可避免的时间延迟。当前的预测控制方法使用从天气预报中借鉴的经验正交函数（EOF）框架，但是现代机器学习的出现和神经网络（NN）的出现为进一步改进提供了空间。在这里，我们评估了神经网络在预测控制中的潜在应用，并强调了它们提供的优势。我们首先展示了它们在线性EOF方法与天空数据之间使用的优于标准截断正则化的正则化，然后证明了NN在模拟数据上建模非线性的能力。这与金字塔波前传感器（PyWFS）的操作高度相关，因为非线性处理将使PyWFS能够以低调制率使用并提供极其敏感的波前测量结果。