Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives

TL;DR

This paper articulates telescope intelligence as a distinct domain within AI-assisted astronomy and maps six core areas: site selection, optical-system calibration, observation scheduling, fault diagnosis, imaging quality optimization, and database intelligence. It surveys historical and contemporary AI methods applied to these areas, highlighting data labeling as a current hotspot and detailing advances in adaptive optics and site seeing. The authors synthesize a trend analysis showing AI methods often surpass traditional approaches in efficiency and accuracy for dome seeing, planning, and data labeling, while outlining future hotspots across large-aperture and space telescopes, interferometry, and AI-driven automation via large language models. This work provides a structured roadmap for researchers and practitioners to prioritize investments, develop interfaces between AI and telescope hardware, and anticipate challenges posed by next-generation facilities and megaconstellations.

Abstract

Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given.

Figures (10)

• Figure 1: Classes of telescope entities
• Figure 2: The environment of some sites, which are local in the high-altitude area or Antarctica
• Figure 3: This method uses the output of the CNN (Convolutional Neural Network) model as input for the Transformer model to achieve cloud classification and recognition in all-sky camera imageryli2022novel
• Figure 4: Schematic diagram of the structure of the Earth's atmosphere
• Figure 5: In focus and defocus star image and aberrated wavefront maps are used to traine the Bi-GRU network, and trained network is used to predict wavefront maps wang2021deep