Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor

Mulet, Bartomeu Pou; Smith, Jeffrey C.; Quiñones, Eduardo; Martínez, Antonio Bascones; Gratadour, Damien

doi:10.1117/12.2627849

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…We are also examining applications of our phase reconstruction in real-time control with impressive early results, 20 , 29 and are encouraged about its applicability given we are measuring an inference time of 0.34 ms on retail desktop GPUs. With optimization and dedicated hardware 30 , 31 and the COSMIC framework platform, 32 wavefront inference from pre-trained networks has potential for hard real-time AO loop control, and not just in postprocessing workflows via PSF reconstruction.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Image-to-image translation for wavefront and point spread function estimation

Smith

Cranney

Gretton

et al. 2023

J. Astron. Telesc. Instrum. Syst.

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We develop and evaluate a new approach to phase estimation for observational astronomy that can be used for accurate point spread function reconstruction. Phase estimation is required where a terrestrial observatory uses an adaptive optics (AO) system to assist astronomers in acquiring sharp, high-contrast images of faint and distant objects. Our approach is to train a conditional adversarial artificial neural network architecture to predict phase using the wavefront sensor data from a closed-loop AO system. We present a detailed simulation study under different turbulent conditions, using the retrieved residual phase to obtain the point spread function of the simulated instrument. Compared to the state-of-the-art model-based approach in astronomy, our approach is not explicitly limited by modeling assumptions, e.g., independence between terms, such as bandwidth and anisoplanatism-and is conceptually simple and flexible.We use the open-source COMPASS tool for end-to-end simulations. On key quality metrics, specifically the Strehl ratio and Halo distribution in our application domain, our approach achieves results better than the model-based baseline.

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Section: Discussion and Future Workmentioning

confidence: 99%

Image-to-image translation for wavefront and point spread function estimation

Smith

Cranney

Gretton

et al. 2023

J. Astron. Telesc. Instrum. Syst.

Self Cite

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“…Pou shows improvements in Strehl for a simulated 8-meter telescope with Shack-Hartmann WFS and PWFS, from 85% Strehl to 91% Strehl calculated at 1650 nm. 41 However, for the Pyramid, this performance required a nonlinear wavefront reconstructor before the RL controller.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Tempestas ex machina: a review of machine learning methods for wavefront control

Fowler,

Landman

2023

Techniques and Instrumentation for Detection of Exoplanets XI

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As we look to the next generation of adaptive optics systems, now is the time to develop and explore the technologies that will allow us to image rocky Earth-like planets; wavefront control algorithms are not only a crucial component of these systems, but can benefit our adaptive optics systems without requiring increased detector speed and sensitivity or more effective and efficient deformable mirrors. To date, most observatories run the workhorse of their wavefront control as a classic integral controller, which estimates a correction from wavefront sensor residuals, and attempts to apply that correction as fast as possible in closed-loop. An integrator of this nature fails to address temporal lag errors that evolve over scales faster than the correction time, as well as vibrations or dynamic errors within the system that are not encapsulated in the wavefront sensor residuals; these errors impact high contrast imaging systems with complex coronagraphs. With the rise in popularity of machine learning, many are investigating applying modern machine learning methods to wavefront control. Furthermore, many linear implementations of machine learning methods (under varying aliases) have been in development for wavefront control for the last 30-odd years. With this work we define machine learning in its simplest terms, explore the most common machine learning methods applied in the context of this problem, and present a review of the literature concerning novel machine learning approaches to wavefront control.

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Model-free reinforcement learning with a non-linear reconstructor for closed-loop adaptive optics control with a pyramid wavefront sensor

Cited by 2 publications

References 27 publications

Image-to-image translation for wavefront and point spread function estimation

Image-to-image translation for wavefront and point spread function estimation

Tempestas ex machina: a review of machine learning methods for wavefront control

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