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Sky survey telescopes play a critical role in modern astronomy, butmisalignment of their optical elements can introduce significant variations inpoint spread functions, leading to reduced data quality. To address this, weneed a method to obtain misalignment states, aiding in the reconstruction ofaccurate point spread functions for data processing methods or facilitatingadjustments of optical components for improved image quality. Since sky surveytelescopes consist of many optical elements, they result in a vast array ofpotential misalignment states, some of which are intricately coupled, posingdetection challenges. However, by continuously adjusting the misalignmentstates of optical elements, we can disentangle coupled states. Based on thisprinciple, we propose a deep neural network to extract misalignment states fromcontinuously varying point spread functions in different field of views. Toensure sufficient and diverse training data, we recommend employing a digitaltwin to obtain data for neural network training. Additionally, we introduce thestate graph to store misalignment data and explore complex relationshipsbetween misalignment states and corresponding point spread functions, guidingthe generation of training data from experiments. Once trained, the neuralnetwork estimates misalignment states from observation data, regardless of theimpacts caused by atmospheric turbulence, noise, and limited spatial samplingrates in the detector. The method proposed in this paper could be used toprovide prior information for the active optics system and the optical systemalignment.

Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks