Challenges of digital twin applications for radio telescopes

Large-aperture, high-frequency radio telescopes are prone to structural deformation under the influence of gravity, temperature, wind load and other environmental factors, which in turn affects the shape of the main reflector and pointing accuracy. Therefore, this paper systematically reviews the virtual-real fusion diagnosis mechanism and application progress of digital twins in radio telescopes. Firstly, the differences and boundaries between digital twins and traditional digital simulation and state monitoring are clarified. Then, the methods in FAST, QTT, and other projects are summarized, and the current main challenges such as multi-source heterogeneous and multi-scale fusion, robust prediction under model uncertainty and real-time constraints, as well as communication delay and system complexity in engineering implementation are summarized. Looking to the future, in order to improve environmental adaptability and high-frequency observation capabilities, combined with deep learning, a mechanism-data deeply coupled digital twin model is constructed to provide new ideas and paths for the robust operation and performance improvement of the next generation of radio telescopes.