【摘要】 绝大多数大型望远镜采用分块镜的设计方案，为了获得优质的成像效果，需要控制分块望远镜系统的piston和tip-tilt误差。神经网络误差检测方法相较于传统的检测方法具有一定优势，但存在仅检测单一类型误差的局限性。本文提出一种基于卷积神经网络的piston和tip-tilt误差同步检测方法，通过在出瞳面设置具有离散孔的光阑，引发分段镜反射的子波发生干涉-衍射现象，构建包含丰富piston和tip-tilt误差信息的数据集。通过粗测网络和精测网络级联，满足大范围和高精度同步检测的需求。结果表明，该方法实现了对输入光源相干长度内纳米级的piston误差检测，并对10μrad范围内的tip-tilt误差实现了亚微弧度检测；对40 dB的CCD噪声表现出良好的抗干扰性，对面形误差的允差为0.05λ₀RMS（λ₀=600 nm）,同时对六子镜系统具有可扩展性。本文方法光路简单，操作便利，具有实际意义。更多还原

【Abstract】 Most large telescopes adopt the design scheme of segmented mirror. In order to obtain high-quality imaging effect, it is necessary to control the piston and tip-tilt errors of segmented telescope system. Compared with traditional detection methods, the error detection method based on neural networks has some advantages, but it is limited to detecting only a single type of error. This paper proposes a method for synchronous detection of piston and tip-tilt errors based on a multi-convolutional neural network. By setting a mask with a sparse sub-pupils configuration at the exit pupil, the sub-waves reflected by the segmented mirrors generate interference-diffraction phenomena, thereby constructing a dataset containing rich piston and tip-tilt errors information. The design includes coarse measurement and fine measurement networks to meet the requirements of large-range and high-precision synchronous detection. Results demonstrate that the method achieves nanometer-level detection of piston errors within the coherent length of the input light source and sub-milliarcsecond detection of tip-tilt errors within a range of 10 μrad. The method exhibits robust resistance to 40 dB CCD noise, a tolerance of 0.05 λ RMS（λ₀=600 nm） for surface shape errors, and portability to six-mirror systems. Additionally, the method has simple optical path, convenient operation and practical significance.更多还原