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Bringing the Visible Universe into Focus with Robo-AO
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Performance of a U-Net-based neural network for predictive adaptive optics.

Justin G Chen, Vinay Shah, Lulu Liu

    Optics Letters
    |May 14, 2021
    PubMed
    Summary

    A novel neural network (NN) significantly improves predictive adaptive optics (AO) for tracking fast-moving satellites. This AI approach reduces wavefront error by 50%, enabling clearer space imaging.

    Area of Science:

    • Astronomy
    • Artificial Intelligence
    • Optical Engineering

    Background:

    • Predictive adaptive optics (AO) is crucial for imaging fast-moving celestial objects.
    • Traditional AO systems face limitations in tracking dynamic targets like low Earth orbit (LEO) satellites.
    • Developing robust AO systems for space debris tracking presents significant challenges.

    Purpose of the Study:

    • To apply a U-Net-based convolutional neural network (NN) for predictive adaptive optics.
    • To enhance the tracking and imaging capabilities for fast-moving targets in LEO.
    • To evaluate the NN's performance against non-predictive methods in real-world astronomical conditions.

    Main Methods:

    • Utilized a U-Net-based convolutional neural network architecture.

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  • Trained the NN predominantly on simulated data.
  • Tested the NN using 1 kHz Shack-Hartmann wavefront sensor data from open-loop observations.
  • Collected data at the Advanced Electro-Optical System facility at Haleakala Observatory.

    • Main Results:

    • Achieved an approximate 50% reduction in mean-squared wavefront error compared to non-predictive approaches.
    • Demonstrated the NN's ability to predict wavefronts up to eight frames into the future.
    • Successfully validated the NN's performance on real-sky data of LEO space debris.
    • Reported the first successful on-sky test of a NN for predictive AO in space tracking.

    Conclusions:

    • The U-Net-based NN shows significant promise for predictive adaptive optics in astronomical applications.
    • This AI-driven approach offers a substantial improvement for tracking and imaging challenging targets like LEO satellites.
    • The study represents a pioneering step in applying neural networks to demanding space-based optical tracking scenarios.