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Register a new userClosed Loop Predictive Control of Adaptive Optics Systems with Convolutional Neural Networks
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Predictive wavefront control is an important and rapidly developing field of adaptive optics (AO). Through the prediction of future wavefront effects, the inherent AO system servo-lag caused by the measurement, computation, and application of the wavefront correction can be significantly mitigated. This lag can impact the final delivered science image, including reduced strehl and contrast, and inhibits our ability to reliably use faint guidestars. We summarize here a novel method for training deep neural networks for predictive control based on an adversarial prior. Unlike previous methods in the literature, which have shown results based on previously generated data or for open-loop systems, we demonstrate our networks performance simulated in closed loop. Our models are able to both reduce effects induced by servo-lag and push the faint end of reliable control with natural guidestars, improving K-band Strehl performance compared to classical methods by over 55% for 16th magnitude guide stars on an 8-meter telescope. We further show that LSTM based approaches may be better suited in high-contrast scenarios where servo-lag error is most pronounced, while traditional feed forward models are better suited for high noise scenarios. Finally, we discuss future strategies for implementing our system in real-time and on astronomical telescope systems.
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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.
One of the primary science goals of the Large UV/Optical/Infrared Surveyor (LUVOIR) mission concept is to detect and characterize Earth-like exoplanets orbiting nearby stars with direct imaging. The success of its coronagraph instrument ECLIPS (Extreme Coronagraph for Living Planetary Systems) depends on the ability to stabilize the wavefront from a large segmented mirror such that optical path differences are limited to tens of picometers RMS during an exposure time of a few hours. In order to relax the constraints on the mechanical stability, ECLIPS will be equipped with a wavefront sensing and control (WS&C) architecture to correct wavefront errors up to temporal frequencies >~1 Hz. These errors may be dominated by spacecraft structural dynamics exciting vibrations at the segmented primary mirror. In this work, we present detailed simulations of the WS&C system within the ECLIPS instrument and the resulting contrast performance. This study assumes wavefront aberrations based on a finite element model of a simulated telescope with spacecraft structural dynamics. Wavefront residuals are then computed according to a model of the adaptive optics system that includes numerical propagation to simulate a realistic wavefront sensor and an analytical model of the temporal performance. An end-to-end numerical propagation model of ECLIPS is then used to estimate the residual starlight intensity distribution at the science detector. We show that the contrast performance depends strongly on the target star magnitude and the spatio-temporal distribution of wavefront errors from the telescope. In cases with significant vibration, we advocate for the use of laser metrology to mitigate high temporal frequency wavefront errors and increase the mission yield.
We present laboratory results of the closed-loop performance of the Magellan Adaptive Optics (AO) Adaptive Secondary Mirror (ASM), pyramid wavefront sensor (PWFS), and VisAO visible adaptive optics camera. The Magellan AO system is a 585-actuator low-emissivity high-throughput system scheduled for first light on the 6.5 meter Magellan Clay telescope in November 2012. Using a dichroic beamsplitter near the telescope focal plane, the AO system will be able to simultaneously perform visible (500-1000 nm) AO science with our VisAO camera and either 10 micron or 3-5 micron science using either the BLINC/MIRAC4 or CLIO cameras, respectively. The ASM, PWS, and VisAO camera have undergone final system tests in the solar test tower at the Arcetri Institute in Florence, Italy, reaching Strehls of 37% in i-band with 400 modes and simulated turbulence of 14 cm ro at v-band. We present images and test results of the assembled VisAO system, which includes our prototype advanced Atmospheric Dispersion Corrector (ADC), prototype calcite Wollaston prisms for SDI imaging, and a suite of beamsplitters, filters, and other optics. Our advanced ADC performs in the lab as designed and is a 58% improvement over conventional ADC designs. We also present images and results of our unique Calibration Return Optic (CRO) test system and the ASM, which has successfully run in closed- loop at 1kHz. The CRO test is a retro reflecting optical test that allows us to test the ASM off-sky in close-loop using an artificial star formed by a fiber source.
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.
The behavior of an adaptive optics (AO) system for ground-based high contrast imaging (HCI) dictates the achievable contrast of the instrument. In conditions where the coherence time of the atmosphere is short compared to the speed of the AO system, the servo-lag error becomes the dominate error term of the AO system. While the AO system measures the wavefront error and subsequently applies a correction (taking a total of 1 to 2 milli-seconds), the atmospheric turbulence above the telescope has changed. In addition to reducing the Strehl ratio, the servo-lag error causes a build-up of speckles along the direction of the dominant wind vector in the coronagraphic image, severely limiting the contrast at small angular separations. One strategy to mitigate this problem is to predict the evolution of the turbulence over the delay. Our predictive wavefront control algorithm minimizes the delay in a mean square sense and has been implemented on the Keck II AO bench. In this paper we report on the latest results of our algorithm and discuss updates to the algorithm itself. We explore how to tune various filter parameters on the basis of both daytime laboratory tests and on-sky tests. We show a reduction in residual-mean-square wavefront error for the predictor compare to the leaky integrator implemented on Keck. Finally, we present contrast improvements for both day time and on-sky tests. Using the L-band vortex coronagraph for Kecks NIRC2 instrument, we find a contrast gain of 2.03 at separation of 3~$lambda/D$ and up to 3 for larger separations (4-6~$lambda/D$).
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