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    We developed a new AI method for the Wide-Field Survey Telescope (WFST) to accurately identify isolated stars. This improves active optics systems by reducing false detections and enabling real-time wavefront correction.

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    Area of Science:

    • Astronomy and Astrophysics
    • Computer Science
    • Optical Engineering

    Background:

    • Active optics systems are crucial for wavefront correction in telescopes.
    • Accurate identification of stellar sources is essential for these systems.
    • Wide-field survey telescopes (WFST) face challenges with identifying sources in complex images.

    Purpose of the Study:

    • To redesign the preprocessing pipeline for the WFST.
    • To improve the accuracy and efficiency of stellar source identification.
    • To enable real-time wavefront correction in active optics systems.

    Main Methods:

    • Integrated a MobileNetV2-based classifier into the WFST preprocessing pipeline.
    • Developed a signal-to-noise ratio-guided selection strategy.
    • Distinguished isolated stars from blended or spurious detections in curvature sensor images.

    Main Results:

    • Achieved over 99% precision for isolated sources and background.
    • Significantly reduced false detections caused by overlapping sources.
    • Demonstrated sub-second CPU latency for real-time processing.

    Conclusions:

    • The MobileNetV2-based classifier and SNR-guided strategy enhance stellar source identification for active optics.
    • The system is validated for real-time applicability and scalability in wide-field telescopes.
    • This approach supports the advancement of future wide-field active optics systems.