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Register a new userData-driven subspace predictive control of adaptive optics for high-contrast imaging
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The search for exoplanets is pushing adaptive optics systems on ground-based telescopes to their limits. One of the major limitations at small angular separations, exactly where exoplanets are predicted to be, is the servo-lag of the adaptive optics systems. The servo-lag error can be reduced with predictive control where the control is based on the future state of the atmospheric disturbance. We propose to use a linear data-driven integral predictive controller based on subspace methods that is updated in real time. The new controller only uses the measured wavefront errors and the changes in the deformable mirror commands, which allows for closed-loop operation without requiring pseudo-open loop reconstruction. This enables operation with non-linear wavefront sensors such as the pyramid wavefront sensor. We show that the proposed controller performs near-optimal control in simulations for both stationary and non-stationary disturbances and that we are able to gain several orders of magnitude in raw contrast. The algorithm has been demonstrated in the lab with MagAO-X, where we gain more than two orders of magnitude in contrast.
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Current and future high-contrast imaging instruments require extreme adaptive optics (XAO) systems to reach contrasts necessary to directly image exoplanets. Telescope vibrations and the temporal error induced by the latency of the control loop limit the performance of these systems. One way to reduce these effects is to use predictive control. We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop predictive control. First, we verify our proposed approach for tip-tilt control in simulations and a lab setup. The results show that this algorithm can effectively learn to mitigate vibrations and reduce the residuals for power-law input turbulence as compared to an optimal gain integrator. We also show that the controller can learn to minimize random vibrations without requiring online updating of the control law. Next, we show in simulations that our algorithm can also be applied to the control of a high-order deformable mirror. We demonstrate that our controller can provide two orders of magnitude improvement in contrast at small separations under stationary turbulence. Furthermore, we show more than an order of magnitude improvement in contrast for different wind velocities and directions without requiring online updating of the control law.
The effects of photon noise, aliasing, wavefront chromaticity and scintillation on the point spread function (PSF) contrast achievable with ground based adaptive optics (AO) are evaluated for different wavefront sensing schemes. I show that a wavefront sensor (WFS) based upon the Zernike phase contrast technique offers the best sensitivity to photon noise at all spatial frequencies, while the Shack-Hartmann WFS is significantly less sensitive. In AO systems performing wavefront sensing in the visible and scientific imaging in the near-IR, the PSF contrast limit is set by the scintillation chromaticity induced by Fresnel propagation through the atmosphere. On a 8m telescope, the PSF contrast is then limited to 1e-4 to 1e-5 in the central arcsecond. Wavefront sensing and scientific imaging should therefore be done at the same wavelength, in which case, on bright sources, PSF contrasts between 1e-6 and 1e-7 can be achieved within 1 arcsecond on a 8m telescope in optical/near-IR. The impact of atmospheric turbulence parameters (seeing, wind speed, turbulence profile) on the PSF contrast is quantified. I show that a focal plane wavefront sensing scheme offers unique advantages, and I discuss how to implement it. Coronagraphic options are also briefly discussed.
Atmospheric wavefront prediction based on previous wavefront sensor measurements can greatly enhance the performance of adaptive optics systems. We propose an optimal linear approach based on the Empirical Orthogonal Functions (EOF) framework commonly employed for atmospheric predictions. The approach offers increased robustness and significant performance advantages over previously proposed wavefront prediction algorithms. It can be implemented as a linear pattern matching algorithm, which decomposes in real time the input (most recent wavefront sensor measurements) into a linear sum of previously encountered patterns, and uses the coefficients of this linear expansion to predict the future state. The process is robust against evolving conditions, unknown spatio-temporal correlations and non-periodic transient events, and enables multiple sensors (for example accelerometers) to contribute to the wavefront estimation. We illustrate the EOFs advantages through numerical simulations, and demonstrate filter convergence within 1 minute on a 1 kHz rate system. We show that the EOFs approach provides significant gains in high contrast imaging by simultaneously reducing residual speckle halo and producing a residual speckle halo that is spatially and temporally uncorrelated.
In this work we explore the possibility of using Recurrence Quantification Analysis (RQA) in astronomical high-contrast imaging to statistically discriminate the signal of faint objects from speckle noise. To this end, we tested RQA on a sequence of high frame rate (1 kHz) images acquired with the SHARK-VIS forerunner at the Large Binocular Telescope. Our tests show promising results in terms of detection contrasts at angular separations as small as $50$ mas, especially when RQA is applied to a very short sequence of data ($2$ s). These results are discussed in light of possible science applications and with respect to other techniques like, for example, Angular Differential Imaging and Speckle-Free Imaging.
The Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) instrument is a multipurpose high-contrast imaging platform designed for the discovery and detailed characterization of exoplanetary systems and serves as a testbed for high-contrast imaging technologies for ELTs. It is a multi-band instrument which makes use of light from 600 to 2500nm allowing for coronagraphic direct exoplanet imaging of the inner 3 lambda/D from the stellar host. Wavefront sensing and control are key to the operation of SCExAO. A partial correction of low-order modes is provided by Subarus facility adaptive optics system with the final correction, including high-order modes, implemented downstream by a combination of a visible pyramid wavefront sensor and a 2000-element deformable mirror. The well corrected NIR (y-K bands) wavefronts can then be injected into any of the available coronagraphs, including but not limited to the phase induced amplitude apodization and the vector vortex coronagraphs, both of which offer an inner working angle as low as 1 lambda/D. Non-common path, low-order aberrations are sensed with a coronagraphic low-order wavefront sensor in the infrared (IR). Low noise, high frame rate, NIR detectors allow for active speckle nulling and coherent differential imaging, while the HAWAII 2RG detector in the HiCIAO imager and/or the CHARIS integral field spectrograph (from mid 2016) can take deeper exposures and/or perform angular, spectral and polarimetric differential imaging. Science in the visible is provided by two interferometric modules: VAMPIRES and FIRST, which enable sub-diffraction limited imaging in the visible region with polarimetric and spectroscopic capabilities respectively. We describe the instrument in detail and present preliminary results both on-sky and in the laboratory.
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