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Register a new userLUVOIR-ECLIPS closed-loop adaptive optics performance and contrast predictions
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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.
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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.
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.
We describe the current performance of the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) instrument on the Subaru telescope on Maunakea, Hawaii and present early science results for SCExAO coupled with the CHARIS integral field spectrograph. SCExAO now delivers H band Strehl ratios up to $sim$ 0.9 or better, extreme AO corrections for optically faint stars, and planet-to-star contrasts rivaling that of GPI and SPHERE. CHARIS yield high signal-to-noise detections and 1.1--2.4 $mu m$ spectra of benchmark directly-imaged companions like HR 8799 cde and kappa And b that clarify their atmospheric properties. We also show how recently published as well as unpublished observations of LkCa 15 lead to a re-evaluation of its claimed protoplanets. Finally, we briefly describe plans for a SCExAO-focused direct imaging campaign to directly image and characterize young exoplanets, planet-forming disks, and (later) mature planets in reflected light.
MagAO is the new adaptive optics system with visible-light and infrared science cameras, located on the 6.5-m Magellan Clay telescope at Las Campanas Observatory, Chile. The instrument locks on natural guide stars (NGS) from 0$^mathrm{th}$ to 16$^mathrm{th}$ $R$-band magnitude, measures turbulence with a modulating pyramid wavefront sensor binnable from 28x28 to 7x7 subapertures, and uses a 585-actuator adaptive secondary mirror (ASM) to provide flat wavefronts to the two science cameras. MagAO is a mutated clone of the similar AO systems at the Large Binocular Telescope (LBT) at Mt. Graham, Arizona. The high-level AO loop controls up to 378 modes and operates at frame rates up to 1000 Hz. The instrument has two science cameras: VisAO operating from 0.5-1 $mu$m and Clio2 operating from 1-5 $mu$m. MagAO was installed in 2012 and successfully completed two commissioning runs in 2012-2013. In April 2014 we had our first science run that was open to the general Magellan community. Observers from Arizona, Carnegie, Australia, Harvard, MIT, Michigan, and Chile took observations in collaboration with the MagAO instrument team. Here we describe the MagAO instrument, describe our on-sky performance, and report our status as of summer 2014.
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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