In this study, we analyze the stability of Zernike coefficient computation using deep learning techniques and propose a new training method for deep learning model that can reliably output higher-order Zernike coefficients. Previous studies have shown that deep learning is a powerful tool for accurately deriving the Zernike coefficients of polygonal mirrors, but reliably extracting higher-order Zernike coefficients remains one of the challenges. To overcome these challenges, we present a new training method for the stability of deep learning model, enabling reliable high-order Zernike coefficient computation. The proposed deep learning model is designed based on the Network-in-Network concept, and a two-stage training process ensures that low-order and high-order Zernike coefficients are simultaneously reliably generated. Experimental results for performance evaluation show that the proposed deep learning model is effective in outputting stable and reliable higher-order Zernike coefficients, especially for polygonal mirrors.
Precise alignment in Korsch telescopes is crucial due to the complex design and intricate aspheric primary mirror. Conventional alignment methods using wavefront sensors often encounter difficulties with obstructions from secondary components, leading to potential inaccuracies and suboptimal solutions. This study introduces a novel alignment methodology utilizing laser radar technology and an integrated metrology system to capture the three-dimensional surface profile of the aspheric primary mirror and other components. The methodology includes strategic placement of multiple laser radars and additional laser trackers and autocollimators for comprehensive measurement and continuous monitoring. This approach significantly reduces alignment errors, enhances precision, and accelerates the alignment process. The results show that deviations of main mirrors under varying gravity conditions closely match finite element analysis, validating the robustness of the alignment in zero-gravity environments. The ability to detect and compensate for gravitational effects ensures optimal performance, highlighting the effectiveness of the proposed methodology. This research presents a significant advancement in optical engineering, providing a reliable and efficient alignment technique for complex optical systems.
In this paper, we propose a noncontact, high-precision method for measuring the flat surface of large-scale optics using laser trackers and spherically mounted reflectors (SMRs) placed on motorized stages. Accurate measurement of surface flatness is critical for the development of optical systems, especially for aligning large-scale astronomical telescopes and space-based instruments. The proposed method can capture high-resolution surface figure of large flat areas, generating dense spatial point clouds. The measurement system consists of laser trackers, SMRs on two-dimensional motorized stages, and a flat mirror. The laser tracker directly measures the position of an SMR and captures an image of the SMR through the reflection from a flat mirror. The motorized stage enables precise and repeatable movement of the optics, allowing for the measurement of the local slope and complete surface figure of the flat mirror. To demonstrate the effectiveness of the proposed method, we conducted a series of measurements using a large flat mirror. The results show that the proposed method can measure the surface figure of a flat mirror with six-degrees-of-freedom, accuracy, and precision. The measurement data obtained from the laser tracker and SMR were compared with those obtained using an interferometer-based measurement system with a parabolic mirror, and the results were found to be in excellent agreement. The proposed method offers a noncontact, high-precision solution for measuring the surface figure of large flat areas and has the potential to significantly improve the manufacturing and testing of large optical systems for astronomy and space-based applications.
This paper presents a new method called ZernikeNet for accurately calculating Zernike coefficients in aspheric optical components. Surface figure error (SFE) measurements obtained using interferometer often include alignment errors and low-order aberrations, such as piston, tip, tilt, and defocus, which need to be removed to effectively analyze high-order aberrations. The traditional method for removing low-order aberrations involves Zernike polynomial fitting to the SFE, but this assumes that the optical component is circular and can be decomposed into an orthogonal basis set of Zernike polynomials. However, for aspheric optical components, the orthogonality of Zernike polynomials may not hold, making it challenging to accurately represent the SFE. To address this challenge, ZernikeNet employs a deep learning-based approach, where interferometer map of the optical component is fed into a multi-layer neural network structure to output a set of 36 Zernike coefficients. The proposed deep learning network is trained using a single-shot metrology approach, where a single input interferometer map is used to generate high-accuracy Zernike coefficients through intentional overfitting. Experimental results using data from aspheric mirror show that ZernikeNet can effectively remove low-order aberrations, leaving only high-order aberrations, resulting in a low residual SFE RMS value. This method offers a significant advantage over traditional Zernike polynomial fitting approaches for optical components with complex shapes, making it a promising tool for the design and analysis of advanced optical systems.
In this study, we investigated the impact of ghost images on the modulation transfer function (MTF) of a Korsch-type telescope using nonsequential ray-tracing simulations and the experimental measurements of the knife-edge method with a collimator and light source targets. Our findings showed that ghost images introduce a directional bias into the edge spread function depending on the field position, which affects the line spread function and MTF. Furthermore, our measurement results demonstrated that ghost images can significantly affect the MTF on the edge field of the green channel. The ghost-to-signal ratio in the multispectral (MS) green channel was approximately 2.5%, which is approximately 0.25% higher than that in the panchromatic channel. To estimate the impact of ghost images in the MS green channel, we performed a parametric analysis using a nonsequential ray-tracing simulation, exploring potential strategies, such as adjusting the window thickness, the distance between the detector and the window, the transmittance of the window surface, and the reflectance of the detector surface. By comparing the positions and intensities of the ghost images obtained from the simulations with those measured experimentally, we identified the simulation input parameters that best reproduced the measured results. Our study provides valuable insight into the importance of managing ghost images when designing and operating Korsch-type telescopes to achieve the optimal image quality.
We propose an alignment strategy that includes optimization criteria and appropriate targets to achieve satisfactory performance both on the ground and in space. The performance of a space telescope can vary significantly based on its assembly and alignment on the ground and its operation in space. Simulations were conducted to study the effects of gravity on a Korsch-type telescope with 0° astigmatism in the primary mirror. The results indicated that gravity influenced overall performance and led to an imbalance in performance across different fields. We propose three optimization criteria: overall, balanced, and good performance in both ground- and space-based environments. To meet these criteria, the telescope was optimized under the influence of gravity. Consequently, the selected optimization target successfully met the criteria by achieving good and balanced performance on the ground and in space. However, typical optimization targets, such as minimizing and designing the RMS wavefront error, are unable to fulfill all three criteria. Therefore, our alignment strategy offers a suitable solution that considers gravitational effects.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.