Application of machine learning in the alignment of off-axis optical system

Lei Yu; Caiwen Ma; Xing Fu; Yamei Yin; Mingqiang Cao

doi:10.1117/12.2606474

24 November 2021 Application of machine learning in the alignment of off-axis optical system

Lei Yu, Caiwen Ma, Xing Fu, Yamei Yin, Mingqiang Cao

Proceedings Volume 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations; 120690P (2021) https://doi.org/10.1117/12.2606474
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Coaxial optical system has a symmetry of revolution. Alignment for this kind of optical system is easy. The desired image quality can be rapidly converged. As for off-axis optical system, traditional optical alignment method can not be used due to the loss of rotational symmetry. Low initial position accuracy makes installation and adjustment more difficult than usual. In this paper, we aim to solve the alignment problem for off-axis optical system with the help of machine learning and its powerful numerical fitting ability. We carried out our research on alignment method for an Gregorian off-axis system. The location of primary mirror is fixed as the optical reference. Alignment process is to adjust posture of secondary mirror to acquire ideal image quality. We use Zemax and Python co-simulation technology to get simulated data. Then multi-layer artificial neural network is utilized to fit the mathematical relationship between misalignments and Zernike coefficients. Given the coefficients, the misalignments can be calculated by the neural network. Finally we conduct alignment experiment to verify the proposed method. The result has proved that this method is a fast and efficient alignment solution for the off-axis optical systems.

Citation Download Citation

Lei Yu, Caiwen Ma, Xing Fu, Yamei Yin, and Mingqiang Cao "Application of machine learning in the alignment of off-axis optical system", Proc. SPIE 12069, AOPC 2021: Novel Technologies and Instruments for Astronomical Multi-Band Observations, 120690P (24 November 2021); https://doi.org/10.1117/12.2606474

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