AI-Powered Low-Order Focal Plane Wavefront Sensing in Infrared
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913548348686336 |
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| author | Taheri, Mojtaba Molahasani, Mahdiyar Ragland, Sam Neichel, Benoit Wizinowich, Peter |
| author_facet | Taheri, Mojtaba Molahasani, Mahdiyar Ragland, Sam Neichel, Benoit Wizinowich, Peter |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_12084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Powered Low-Order Focal Plane Wavefront Sensing in Infrared Taheri, Mojtaba Molahasani, Mahdiyar Ragland, Sam Neichel, Benoit Wizinowich, Peter Instrumentation and Methods for Astrophysics 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. |
| title | AI-Powered Low-Order Focal Plane Wavefront Sensing in Infrared |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2410.12084 |