Improving Astronomy Image Quality Through Real-time Wavefront Estimation

Thomas, David L.; Meyers, J.; Kahn, S. M.

doi:10.1109/cvprw53098.2021.00236

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2023

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“…Optimizing the architecture for real-time inference would require considerable work and is outside the scope of this analysis. Larger models have been demonstrated to run at 6 ms on NVIDIA V100 GPUs 37 suggesting our model is likely to be able to be optimized to the required latency (≤1 ms) on more modern GPUs.…”

Section: Neural Network Architecturementioning

confidence: 99%

Mitigating the nonlinearities in a pyramid wavefront sensor

Archinuk,

Hafeez,

Fabbro

et al. 2023

J. Astron. Telesc. Instrum. Syst.

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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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