Research on the method for simultaneously detecting piston and tip-tilt errors of segmented telescopes based on multiple CNNs
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.
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