基于卷积神经网络的自适应光学系统闭环预测控制
Closed Loop Predictive Control of Adaptive Optics Systems with Convolutional Neural Networks
摘要：
波前预测控制是自适应光学的一个重要而迅速发展的领域。通过对未来波前效应的预测，可以显著减轻由波前校正的测量、计算和应用引起的固有AO系统伺服滞后。这种滞后会影响最终交付的科学图像，包括降低strehl和对比度，并抑制我们可靠使用微弱导航星的能力。我们在这里总结了一种基于对抗性先验知识训练深度神经网络进行预测控制的新方法。与文献中以前的方法不同，这些方法基于以前生成的数据或开环系统显示了结果，我们证明了我们的网络在闭环中模拟的性能。我们的模型能够减少伺服滞后引起的影响，并推动自然guidestars的可靠控制的微弱端，与经典方法相比，在8米望远镜上，16等引导星的K波段Strehl性能提高了55%以上。我们进一步表明，基于LSTM的方法可能更适合伺服滞后误差最明显的高对比度场景，而传统的前馈模型更适合高噪声场景。最后，我们讨论了未来的战略，实现我们的系统在实时和天文望远镜系统。
英文摘要：
Predictive wavefront control is an important and rapidly developing field of adaptive optics (AO). Through the prediction of future wavefront effects, the inherent AO system servo-lag caused by the measurement, computation, and application of the wavefront correction can be significantly mitigated. This lag can impact the final delivered science image, including reduced strehl and contrast, and inhibits our ability to reliably use faint guidestars. We summarize here a novel method for training deep neural networks for predictive control based on an adversarial prior. Unlike previous methods in the literature, which have shown results based on previously generated data or for open-loop systems, we demonstrate our network's performance simulated in closed loop. Our models are able to both reduce effects induced by servo-lag and push the faint end of reliable control with natural guidestars, improving K-band Strehl performance compared to classical methods by over 55% for 16th magnitude guide stars on an 8-meter telescope. We further show that LSTM based approaches may be better suited in high-contrast scenarios where servo-lag error is most pronounced, while traditional feed forward models are better suited for high noise scenarios. Finally, we discuss future strategies for implementing our system in real-time and on astronomical telescope systems.
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。
