Adaptive Threshold Wave Front Sensing for Portable Adaptive Optics System Driven by Hybrid Parallel Technique

Abstract Our Portable Adaptive Optics (PAO) system designed for high-contrast imaging of exoplanets with current 2–4 m class telescopes achieves a correction speed of nearly 1000 Hz, utilizing a Shack–Hartmann Wave Front Sensor (WFS) in a 9 × 9 sub-aperture configuration. As we look towards adapting the PAO system for larger telescopes, an increase in the number of sub-apertures in the WFS and enhanced precision in wave front detection are imperative. Originally programmed in LabVIEW, our initial PAO software is based on a traditional centroid calculation module for nighttime wave front sensing and lacks adaptive processing of background noise. To address these limitations and to boost the PAO system's performance and accuracy in wave front detection, we propose a compressive neural network (Th-Net) combined with a specialized hybrid parallel programming approach for wave front detection. Our experimental results indicate that this hybrid parallel technique and Th-Net significantly enhance the PAO system's operational speed and wave front detection precision under uneven background noise. This work paves the way so that a duplicable and low-cost PAO system can be used for direct imaging of exoplanets with large telescopes.