Nonlinear Wave Front Reconstruction from a Pyramid Sensor using Neural Networks

Wong, Alison P.; Norris, Barnaby R. M.; Deo, Vincent; Tuthill, Peter G.; Scalzo, Richard; Sweeney, David; Ahn, Kyohoon; Lozi, Julien; Vievard, Sébastien; Guyon, Olivier

doi:10.1088/1538-3873/acfdcb

Cited by 2 publications

(1 citation statement)

References 58 publications

(41 reference statements)

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“…Lately, NNs, and the more modern deep NNs, have also been used for the wavefront reconstruction step (see, e.g., Refs. [35][36][37][38]. The results indicate that NN reconstruction is less sensitive to non-linearity and increases the operational range of the pyramid WFS.…”

Section: Related Workmentioning

confidence: 99%

Laboratory experiments of model-based reinforcement learning for adaptive optics control

Nousiainen,

Engler,

Kasper

et al. 2024

J. Astron. Telesc. Instrum. Syst.

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Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from their host stars, making their detection difficult. Consequently, the adaptive optics (AO) system's control algorithm must be carefully designed to distinguish the exoplanet from the residual light produced by the host star. A promising avenue of research to improve AO control builds on datadriven control methods, such as reinforcement learning (RL). RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment. Thus, RL can be seen as an automated approach to AO control, where its usage is entirely a turnkey operation. In particular, model-based RL has been shown to cope with temporal and misregistration errors. Similarly, it has been demonstrated to adapt to nonlinear wavefront sensing while being efficient in training and execution. In this work, we implement and adapt an RL method called policy optimization for AO (PO4AO) to the GPU-based high-order adaptive optics testbench (GHOST) test bench at ESO headquarters, where we demonstrate a strong performance of the method in a laboratory environment. Our implementation allows the training to be performed parallel to inference, which is crucial for on-sky operation. In particular, we study the predictive and self-calibrating aspects of the method. The new implementation on GHOST running PyTorch introduces only around 700 μs of in addition to hardware, pipeline, and Python interface latency. We open-source well-documented code for the implementation and specify the requirements for the RTC pipeline. We also discuss the important hyperparameters of the method and how they affect the method. Further, the paper discusses the source of the latency and the possible paths for a lower latency implementation.

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Section: Related Workmentioning

confidence: 99%

Laboratory experiments of model-based reinforcement learning for adaptive optics control

Nousiainen,

Engler,

Kasper

et al. 2024

J. Astron. Telesc. Instrum. Syst.

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Making the unmodulated Pyramid wavefront sensor smart

Landman,

Haffert,

Males

et al. 2024

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Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as a primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. Most instruments try to increase its linearity range by using dynamic modulation, but this leads to decreased sensitivity, most prominently for low-order modes, and makes it blind to petal-piston modes. In the push toward high-contrast imaging of fainter stars and deeper contrasts, there is a strong interest in using the PWFS in its unmodulated form. Here, we present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of the Magellan Adaptive Optics eXtreme (MagAO-X) system based on convolutional neural networks (CNNs). We show that our nonlinear reconstructor has a dynamic range of >600 nm root-mean-square (RMS), significantly outperforming the linear reconstructor that only has a 50 nm RMS dynamic range. The reconstructor behaves well in closed loop and can obtain >80<!PCT!> Strehl at 875 nm under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor also achieves the theoretical sensitivity limit of a PWFS, showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN’s computational time is 690 mu s, which enables loop speeds of >1 kHz. On-sky tests are foreseen soon and will be important for pushing future high-contrast imaging instruments toward their limits.

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Nonlinear Wave Front Reconstruction from a Pyramid Sensor using Neural Networks

Cited by 2 publications

References 58 publications

Laboratory experiments of model-based reinforcement learning for adaptive optics control

Laboratory experiments of model-based reinforcement learning for adaptive optics control

Making the unmodulated Pyramid wavefront sensor smart

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