Almost all current and future high-contrast imaging instruments will use a Pyramid wavefront sensor (PWFS) as primary or secondary wavefront sensor. The main issue with the PWFS is its nonlinear response to large phase aberrations, especially under strong atmospheric turbulence. In this talk, we will present closed-loop lab results of a nonlinear reconstructor for the unmodulated PWFS of MagAO-X based on Convolutional Neural Networks. We show that our nonlinear reconstructor has a dynamic range of >600 nm 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% Strehl under a large variety of conditions and reaches higher Strehl ratios than the linear reconstructor under all simulated conditions. The CNN reconstructor implementation also achieves the theoretical sensitivity limit of a pyramid wavefront sensor showing that it does not lose its sensitivity in exchange for dynamic range. The current CNN’s computational time is 690 us which enables systems to run at >1 kHz. We will also discuss the real-time implementation on MagAO-X and show preliminary on-sky tests.
To reduce the amount of stellar light for exoplanet detection, coronagraphs feature amplitude masks in pupils plane(s) and/or focal plane(s), where a large fraction of photons are stopped -- and generally not used. Here, we give an overview of where potentially useful stellar (and circumstellar) photons are lost. We review existing concepts that use these lost photons, and propose generic strategies to make use of them for various applications. We particularly focus on wavefront sensing applications, but also explore how these photons can be used for calibration measurements, or for additional scientific observations.
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.
The detection and characterization of Earth-like exoplanets around Sun-like stars is a primary science motivation for the Habitable Worlds Observatory. However, the current best technology is not yet advanced enough to reach the 10−10 contrasts at close angular separations and at the same time remain insensitive to low-order aberrations, as would be required to achieve high-contrast imaging of exo-Earths. Photonic technologies could fill this gap, potentially doubling exo-Earth yield. We review current work on photonic coronagraphs and investigate the potential of hybridized designs which combine both classical coronagraph designs and photonic technologies into a single optical system. We present two possible systems. First, a hybrid solution which splits the field of view spatially such that the photonics handle light within the inner working angle and a conventional coronagraph that suppresses starlight outside it. Second, a hybrid solution where the conventional coronagraph and photonics operate in series, complementing each other and thereby loosening requirements on each subsystem. As photonic technologies continue to advance, a hybrid or fully photonic coronagraph holds great potential for future exoplanet imaging from space.
Looking to the future of exo-Earth imaging from the ground, core technology developments are required in visible Extreme Adaptive Optics (ExAO) to enable the observation of atmospheric features such as oxygen on rocky planets in visible light. UNDERGROUND (Ultra-fast AO techNology Determination for Exoplanet imageRs from the GROUND), a collaboration built in Feb. 2023 at the Optimal Exoplanet Imagers Lorentz Workshop, aims to (1) motivate oxygen detection in Proxima Centauri b and analogs as an informative science case for high-contrast imaging and direct spectroscopy, (2) overview the state of the field with respect to visible exoplanet imagers, and (3) set the instrumental requirements to achieve this goal and identify what key technologies require further development.
High-contrast imaging instruments need extreme wavefront control to directly image exoplanets. This requires highly sensitive wavefront sensors which optimally make use of the available photons to sense the wavefront. Here, we propose to numerically optimize Fourier-filtering wavefront sensors using automatic differentiation. First, we optimize the sensitivity of the wavefront sensor for different apertures and wavefront distributions. We find sensors that are more sensitive than currently used sensors and close to the theoretical limit, under the assumption of monochromatic light. Subsequently, we directly minimize the residual wavefront error by jointly optimizing the sensing and reconstruction. This is done by connecting differentiable models of the wavefront sensor and reconstructor and alternatingly improving them using a gradient-based optimizer. We also allow for nonlinearities in the wavefront reconstruction using Convolutional Neural Networks, which extends the design space of the wavefront sensor. Our results show that optimization can lead to wavefront sensors that have improved performance over currently used wavefront sensors. The proposed approach is flexible, and can in principle be used for any wavefront sensor architecture with free design parameters.
KEYWORDS: Optical spheres, Sensors, Planets, Spectrographs, Iterated function systems, Stars, Spectral resolution, Coronagraphy, Adaptive optics, Signal to noise ratio
MedRes is a proposed MEDium RESolution integral field spectrograph for upgrading SPHERE, the high contrast instrument for the ESO VLT telescope. MedRes is actually thought of as a potential Visitor Instrument with the scope to provide high contrast diffraction limited medium-high resolution spectra (R ≥ 1000) over a reasonably large field of view (a square with a side of at least 0.4) and across the spectral region 1.2-1.65 microns. Two main science objectives are driving the proposition for such an instrument on SPHERE. First of all, MedRes shall improve the detection of previously unknown giant planets (contrast 10−5 , goal 10−6 ), in particular accreting planets, at small separation from the star (< 0.2”, goal, 0.1”). And second, MedRes will boost the characterisation of known (faint) planets at a spectral resolution substantially higher than currently possible with SPHERE IFS (R ~ 35 − 50) and for contrasts much better than achievable with IRDIS Long Slit Spectroscopy (LSS) at small separations. The design will be optimised for SPHERE, fully exploiting the capabilities offered by a second stage Adaptive Optics (SAXO+) and complementing the niches of IRDIS, IFS and HiRise in the near IR channel. A preliminary optomechanical design and simulations of performance will be presented.
SPHERE+ is a proposed upgrade of the SPHERE instrument at the VLT, which is intended to boost the current performances of detection and characterization for exoplanets and disks. SPHERE+ will also serve as a demonstrator for the future planet finder (PCS) of the European ELT. The main science drivers for SPHERE+ are 1/ to access the bulk of the young giant planet population down to the snow line (3 − 10 au), to bridge the gap with complementary techniques (radial velocity, astrometry); 2/ to observe fainter and redder targets in the youngest (1 − 10 Myr) associations compared to those observed with SPHERE to directly study the formation of giant planets in their birth environment; 3/ to improve the level of characterization of exoplanetary atmospheres by increasing the spectral resolution in order to break degeneracies in giant planet atmosphere models. Achieving these objectives requires to increase the bandwidth of the xAO system (from ~1 to 3 kHz) as well as the sensitivity in the infrared (2 to 3 mag). These features will be brought by a second stage AO system optimized in the infrared with a pyramid wavefront sensor. As a new science instrument, a medium resolution integral field spectrograph will provide a spectral resolution from 1000 to 5000 in the J and H bands. This paper gives an overview of the science drivers, requirements and key instrumental tradeoff that were done for SPHERE+ to reach the final selected baseline concept.
Current and future high-contrast imaging instruments require extreme adaptive optics systems to reach contrasts necessary to directly imaged 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.
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. Optimization of the (predictive) control algorithm is crucial in reducing these effects. We describe how model-free Reinforcement Learning can be used to optimize a Recurrent Neural Network controller for closed-loop adaptive optics control. 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 suppress a combination of tip-tilt vibrations. Furthermore, we report decreased residuals for power-law input turbulence compared to an optimal gain integrator. Finally, we demonstrate that the controller can learn to identify the parameters of a varying vibration without requiring online updating of the control law. We conclude that Reinforcement Learning is a promising approach towards data-driven predictive control; future research will apply this approach to the control of high-order deformable mirrors.
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