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作 者:李响 赵伟瑞[1] Li Xiang;Zhao Weirui(School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China)
出 处:《仪器仪表学报》2024年第3期188-197,共10页Chinese Journal of Scientific Instrument
基 金:国家自然科学基金(11874086)项目资助。
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。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 10μrad.The method exhibits robust resistance to 40 dB CCD noise,a tolerance of 0.05λRMS(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.
关 键 词:piston误差 tip-tilt误差 分块镜 卷积神经网络
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