Perception of Misalignment States for Sky Survey Telescopes with the
  Digital Twin and the Deep Neural Networks
Miao Zhang Peng Jia Zhengyang Li Wennan Xiang Jiameng Lv Rui Sun
Sky survey telescopes play a critical role in modern astronomy, but
misalignment of their optical elements can introduce significant variations in
point spread functions, leading to reduced data quality. To address this, we
need a method to obtain misalignment states, aiding in the reconstruction of
accurate point spread functions for data processing methods or facilitating
adjustments of optical components for improved image quality. Since sky survey
telescopes consist of many optical elements, they result in a vast array of
potential misalignment states, some of which are intricately coupled, posing
detection challenges. However, by continuously adjusting the misalignment
states of optical elements, we can disentangle coupled states. Based on this
principle, we propose a deep neural network to extract misalignment states from
continuously varying point spread functions in different field of views. To
ensure sufficient and diverse training data, we recommend employing a digital
twin to obtain data for neural network training. Additionally, we introduce the
state graph to store misalignment data and explore complex relationships
between misalignment states and corresponding point spread functions, guiding
the generation of training data from experiments. Once trained, the neural
network estimates misalignment states from observation data, regardless of the
impacts caused by atmospheric turbulence, noise, and limited spatial sampling
rates in the detector. The method proposed in this paper could be used to
provide prior information for the active optics system and the optical system
alignment.