The Active Optics System of the Vera C. Rubin Observatory (Rubin) uses information provided by four wave front sensors to determine deviations between the reconstructed wave front and the ideal wave front. The observed deviations are used to adjust the control parameters of the optical system to maintain image quality across the 3.5 field of view. The baseline approach from the project is to obtain amplitudes of the Zernike polynomials describing the distorted wave front from out-of-focus images collected by the wave front sensors. These Zernike amplitudes are related via an “influence matrix” to the control parameters necessary to correct the wave front. In this paper, we use deep-learning methods to extract the control parameters directly from the images captured by the wave front sensors. Our neural net model uses anti-aliasing pooling to boost performance, and a domain-specific loss function to aid learning and generalization. The accuracy of the control parameters derived from our model exceeds Rubin requirements even in the presence of full-moon background levels and mis-centering of reference stars. Although the training process is time consuming, model evaluation requires only a few milliseconds. This low latency should allow for the correction of the optical configuration during the readout and slew interval between successive exposures.

Vera C. Rubin 天文台 (Rubin) 的有源光学系统使用四个波前传感器提供的信息来确定重建波前与理想波前之间的偏差。观察到的偏差用于调整光学系统的控制参数，以保持 3.5 视场的图像质量。该项目的基线方法是从波前传感器收集的离焦图像中获取描述失真波前的泽尼克多项式的幅度。这些 Zernike 振幅通过“影响矩阵”与校正波前所需的控制参数相关。在本文中，我们使用深度学习方法直接从波前传感器捕获的图像中提取控制参数。我们的神经网络模型使用抗锯齿池来提高性能，并使用特定领域的损失函数来帮助学习和泛化。即使在存在满月背景水平和参考星中心错误的情况下，从我们的模型得出的控制参数的准确性也超过了鲁宾的要求。虽然训练过程很耗时，但模型评估只需要几毫秒。这种低延迟应该允许在连续曝光之间的读出和转换间隔期间校正光学配置。虽然训练过程很耗时，但模型评估只需要几毫秒。这种低延迟应该允许在连续曝光之间的读出和转换间隔期间校正光学配置。虽然训练过程很耗时，但模型评估只需要几毫秒。这种低延迟应该允许在连续曝光之间的读出和转换间隔期间校正光学配置。