Abstract
The imaging quality of a telescope directly affects the reliability of astronomical research. Through the monitoring and diagnosis of imaging quality, the cause of the deterioration of imaging quality can be found in time, which is essential for ensuring the peaking performance of the telescope and high-quality imaging. Moreover, these operations are complex and crucial for achieving high-quality imaging of future giant telescope systems involving active optics, adaptive optics, and other advanced techniques. We propose a three-component method based on cutting-edge artificial intelligence technology to real-time monitor and efficiently diagnose the telescope image quality. The first component, an image quality monitoring system, monitors and outputs the telescope’s image quality. The second component is a query system with a knowledge graph, which outputs the node chains as the possible cause of poor image quality based on the input. The third component, a final estimator, uses the node parameter, which contains historical fault data and real-time updated data from sensors, to give the probability of each node chain. We construct and test the system in the Large Sky Area Multi-Object Fiber Spectroscopy Telescope.