We apply a U-Net-based convolutional neural network (NN) architecture to the problem of predictive adaptive optics (AO) for tracking and imaging fast-moving targets, such as satellites in low Earth orbit (LEO). We show that the fine-tuned NN is able to achieve an approximately 50% reduction in mean-squared wavefront error over non-predictive approaches while predicting up to eight frames into the future. These results were obtained when the NN, trained mostly on simulated data, tested its performance on 1 kHz Shack–Hartmann wavefront sensor data collected in open-loop at the Advanced Electro-Optical System facility at Haleakala Observatory while the telescope tracked a naturally illuminated piece of LEO space debris. We report, to our knowledge, the first successful test of a NN for the predictive AO application using on-sky data, as well as the first time such a network has been developed for the more stressing space tracking application.

中文翻译：

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基于U-Net的神经网络用于预测自适应光学的性能

我们将基于U-Net的卷积神经网络（NN）体系结构应用于预测自适应光学（AO）问题，以跟踪和成像快速移动的目标，例如低地球轨道（LEO）中的卫星。我们表明，经过微调的NN能够比非预测方法减少约50％的均方波前误差，同时还能预测多达八帧。当NN主要在模拟数据上进行训练，并在哈雷阿卡拉天文台的先进电子光学系统设施以开环方式收集的1 kHz Shack-Hartmann波前传感器数据上测试其性能时，而望远镜跟踪的是自然照明的物体，则获得了这些结果。 LEO空间碎片。据我们所知，我们使用天空数据首次成功地对预测性AO应用进行了NN的成功测试，