| Download | - View final version: Mitigating the nonlinearities in a pyramid wavefront sensor (PDF, 8.8 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1117/1.JATIS.9.4.049005 |
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| Author | Search for: Archinuk, Finn1; Search for: Hafeez, Rehan1; Search for: Fabbro, Sébastien1; Search for: Teimoorinia, Hossen1; Search for: Véran, Jean-Pierre1 |
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| Affiliation | - National Research Council of Canada. Herzberg Astronomy and Astrophysics
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| Format | Text, Article |
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| Subject | adaptive optics; machine learning; wavefront reconstruction; astronomy; wavefront errors; wavefront sensors; wavefronts; education and training; data modeling |
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| Abstract | For natural guide star adaptive optics (AO) systems, pyramid wavefront sensors (PWFSs) can provide a significant increase in sensitivity over the traditional Shack–Hartmann but at the cost of a reduced linear range. When using a linear reconstructor, nonlinearities result in wavefront estimation errors, which can have a significant impact on the image quality delivered by the AO system. We simulate a wavefront passing through a PWFS under varying observing conditions to explore the possibility of using a nonlinear machine learning model to estimate wavefront errors and compare with a linear reconstruction. We find significant potential improvements in delivered image quality even with computationally simple models, underscoring the need for further investigation of this approach. |
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| Publication date | 2023-12-28 |
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| Publisher | SPIE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c25bbc1b-8ba3-49c3-a7bc-06fbaf0eee1f |
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| Record created | 2024-05-02 |
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| Record modified | 2024-05-02 |
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