论文标题
基于傅立叶的波前传感器的卷积神经网络的非线性波前重建
Nonlinear wavefront reconstruction with convolutional neural networks for Fourier-based wavefront sensors
论文作者
论文摘要
基于傅立叶的波前传感器，例如金字塔波前传感器（PWFS），是由于其高灵敏度而对高对比度成像的当前偏好。但是，这些波前传感器具有固有的非线性，该非线性限制了可以使用常规线性重建方法来准确估计传入波前畸变的范围。我们建议使用卷积神经网络（CNN）进行波前传感器测量的非线性重建。证明CNN可用于准确地重建模拟和实验室实现中的非线性。我们表明，仅使用CNN进行重建会导致在模拟大气湍流下次优闭环性能。但是，证明使用CNN在线性模型之上估计非线性误差项会导致改进的模拟自适应光学系统的有效动态范围。在非线性误差相关的条件下，较大的有效动态范围会导致较高的StreHL比率。这将使当前和未来的大型天文望远镜能够在更广泛的大气条件下工作，因此减少了此类设施的昂贵停机时间。
Fourier-based wavefront sensors, such as the Pyramid Wavefront Sensor (PWFS), are the current preference for high contrast imaging due to their high sensitivity. However, these wavefront sensors have intrinsic nonlinearities that constrain the range where conventional linear reconstruction methods can be used to accurately estimate the incoming wavefront aberrations. We propose to use Convolutional Neural Networks (CNNs) for the nonlinear reconstruction of the wavefront sensor measurements. It is demonstrated that a CNN can be used to accurately reconstruct the nonlinearities in both simulations and a lab implementation. We show that solely using a CNN for the reconstruction leads to suboptimal closed loop performance under simulated atmospheric turbulence. However, it is demonstrated that using a CNN to estimate the nonlinear error term on top of a linear model results in an improved effective dynamic range of a simulated adaptive optics system. The larger effective dynamic range results in a higher Strehl ratio under conditions where the nonlinear error is relevant. This will allow the current and future generation of large astronomical telescopes to work in a wider range of atmospheric conditions and therefore reduce costly downtime of such facilities.
