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arXiv · 2410.12084

AI-Powered Low-Order Focal Plane Wavefront Sensing in Infrared

Mojtaba Taheri·Mahdiyar Molahasani·Sam Ragland·Benoit Neichel·Peter Wizinowich
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Abstract

Adaptive optics (AO) systems are crucial for high-resolution astronomical observations by compensating for atmospheric turbulence. While laser guide stars (LGS) address high-order wavefront aberrations, natural guide stars (NGS) remain vital for low-order wavefront sensing (LOWFS). Conventional NGS-based methods like Shack-Hartmann sensors have limitations in field of view, sensitivity, and complexity. Focal plane wavefront sensing (FPWFS) offers advantages, including a wider field of view and enhanced signal-to-noise ratio, but accurately estimating low-order modes from distorted point spread functions (PSFs) remains challenging. We propose an AI-powered FPWFS method specifically for low-order mode estimation in infrared wavelengths. Our approach is trained on simulated data and validated on on-telescope data collected from the Keck I adaptive optic (K1AO) bench calibration source in K-band. By leveraging the enhanced signal-to-noise ratio in the infrared and the power of AI, our method overcomes the limitations of traditional LOWFS techniques. This study demonstrates the effectiveness of AI-based FPWFS for low-order wavefront sensing, paving the way for more compact, efficient, and high-performing AO systems for astronomical observations.

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Mojtaba Taheri, Mahdiyar Molahasani, Sam Ragland, Benoit Neichel, Peter Wizinowich. 2024-10-15. AI-Powered Low-Order Focal Plane Wavefront Sensing in Infrared. https://doi.org/10.1117/12.3020402

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