The Shack–Hartmann wavefront sensor (SHWFS) is widely utilized for ocular aberration measurement. However, large ocular aberrations caused by individual differences can easily make the spot move out of the range of the corresponding sub-aperture in SHWFS, rendering the traditional centroiding method ineffective. This study applied a novel convolutional neural network (CNN) model to wavefront sensing for large dynamic ocular aberration measurement. The simulation results demonstrate that, compared to the modal method, the dynamic range of our method for main low-order aberrations in ocular system is increased by 1.86 to 43.88 times in variety. Meanwhile, the proposed method also has the best measurement accuracy, and the statistical root mean square (RMS) of the residual wavefronts is 0.0082 ± 0.0185 $\lambda$ (mean ± standard deviation). The proposed method generally has a higher accuracy while having a similar or even better dynamic range as compared to traditional large-dynamic schemes. On the other hand, compared with recently developed deep learning methods, the proposed method has a much larger dynamic range and better measurement accuracy.

As next-generation large-aperture telescopes, synthetic aperture is a promising method for realizing high resolution observations. Co-phasing the misaligned segmented aperture is an important procedure for high-resolution observations with segmented telescopes. In this paper, a piston error detection method is proposed based on two interference patterns. Two interference patterns are generated by using a lens placed across two adjacent pupils in the exit pupil plane at two wavelengths and a method based on phase retrieval technique is proposed to extract the piston error from the two interference patterns. The introduction of dual-wavelength in the scheme overcomes the 2π ambiguities problem and expands the piston error detection range. Meanwhile, the proposed piston error extraction method based on phase retrieval technique allows high precision measurement of the piston error and is robust to offset lens. Various simulations are demonstrated and the feasibility of the proposed piston error detection method is validated.

The resolution of a telescope is closely related to its aperture size; however, the aperture of a single primary mirror telescope cannot be indefinitely enlarged due to design and manufacturing constraints. Segmented mirror technology can achieve the same resolution as a single primary mirror of equivalent aperture, provided that the segments are co-phased correctly. This paper proposes a method for high-precision detection of piston errors in segmented mirror telescope systems, based on far-field information and transfer learning. By training a ResNet-18 network model, this method can predict piston errors with high precision within 10 ms of a single-frame far-field diffraction image. Simulation results demonstrate that the method is robust to tip-tilt errors, wavefront aberrations, and noise. This approach is simple, fast, highly accurate in detection, and resistant to noise, providing a new solution for piston error detection in segmented mirror systems.

The structural deformations induced by rocket launch vibrations, on-orbit thermal gradients, and gravitation fluctuations can lead to significant deployment errors for large-aperture, segmented space telescopes. As the size and number of segments increase in future telescopes, the optical-based methods for detecting deployment errors suffer from the range limitations of the millimeter scale and time-consuming processes of the month scale. To address this, we propose a new method for rapid-deployment error detection based on long-range, high-precision capacitive edge sensors. These sensors feature a measurement range of ±13 mm, with a precision better than 7.3 nm, enabling efficient and simultaneous error detection across all segments. This approach significantly reduces the time and steps required compared to traditional optical methods. Through experimental validation, the designed system demonstrated the ability to detect and correct large deployment errors and maintain co-phasing precision, meeting the stringent requirements for future space telescopes. The proposed sensor system enhances deployment efficiency, offering a viable solution for the next generation of segmented space telescopes.