Adaptive optics are techniques used for processing the spatial resolution of astronomical images taken from large ground-based telescopes. In this work, computational results are presented for a modified curvature sensor, the tomographic pupil image wavefront sensor (TPI-WFS), which measures the turbulence of the atmosphere, expressed in terms of an expansion over Zernike polynomials. Convolutional neural networks (CNN) are presented as an alternative to the TPI-WFS reconstruction. This technique is a machine learning model of the family of artificial neural networks, which are widely known for its performance as modeling and prediction technique in complex systems. Results obtained from the reconstruction of the networks are compared with the TPI-WFS reconstruction by estimating errors and optical measurements (root mean square error, mean structural similarity and Strehl ratio). The reconstructed wavefronts from both techniques are compared for wavefronts of 153 Zernike modes. For this case, a detailed comparison and grid search to find the most suitable neural network is performed, searching between multi-layer perceptron, CNN and recurrent networks topologies. In general, the best network was a CNN trained for TPI-WFS reconstruction, achieving better performance than the reconstruction software from TPI-WFS in most of the turbulent profiles, but the most significant improvements were found for higher turbulent profiles that have the lowest r0 values.

中文翻译：

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使用CNN补偿散焦瞳孔图像波前传感器的大气湍流

自适应光学器件是用于处理从大型地面望远镜拍摄的天文图像的空间分辨率的技术。在这项工作中，提出了一种改进的曲率传感器，断层摄影瞳孔图像波前传感器（TPI-WFS）的计算结果，该传感器测量大气的湍流，以Zernike多项式的展开表示。卷积神经网络（CNN）被提出作为TPI-WFS重建的替代方法。该技术是人工神经网络家族的机器学习模型，由于其在复杂系统中的建模和预测技术的性能而广为人知。通过估算误差和光学测量值（均方根误差，平均结构相似性和斯特列尔比）。比较了两种技术重建的波前与153种Zernike模式的波前。对于这种情况，将执行详细的比较和网格搜索以找到最合适的神经网络，从而在多层感知器，CNN和循环网络拓扑之间进行搜索。通常，最好的网络是经过CNN培训的TPI-WFS重建网络，在大多数湍流剖面中均比TPI-WFS重建软件具有更好的性能，但对于r0最低的较高湍流剖面，发现了最显着的改进。价值观。进行了详细的比较和网格搜索，以找到最合适的神经网络，并在多层感知器，CNN和循环网络拓扑之间进行搜索。通常，最好的网络是经过CNN培训的TPI-WFS重建网络，在大多数湍流剖面中均比TPI-WFS重建软件具有更好的性能，但对于r0最低的较高湍流剖面，发现了最显着的改进。价值观。进行了详细的比较和网格搜索，以找到最合适的神经网络，并在多层感知器，CNN和循环网络拓扑之间进行搜索。通常，最好的网络是经过CNN培训的TPI-WFS重建网络，在大多数湍流剖面中均比TPI-WFS重建软件具有更好的性能，但对于r0最低的较高湍流剖面，发现了最显着的改进。价值观。