Implicit electric field Conjugation: Data-driven focal plane control

Direct imaging of Earth-like planets is one of the main science cases for the next generation of extremely large telescopes. This is very challenging due to the star-planet contrast that must be overcome. Most current high-contrast imaging instruments are limited in sensitivity at small angular separations due to non-common path aberrations (NCPA). The NCPA leak through the coronagraph and create bright speckles that limit the on-sky contrast and therefore also the post-processed contrast. We aim to remove the NCPA by active focal plane wavefront control using a data-driven approach. We developed a new approach to dark hole creation and maintenance that does not require an instrument model. This new approach is called implicit Electric Field Conjugation (iEFC) and it can be empirically calibrated. This makes it robust for complex instruments where optical models might be difficult to realize. Numerical simulations have been used to explore the performance of iEFC for different coronagraphs. The method was validated on the internal source of the Magellan Adaptive Optics eXtreme (MagAO-X) instrument to demonstrate iEFC's performance on a real instrument. Numerical experiments demonstrate that iEFC can achieve deep contrast below $10^{-9}$ with several coronagraphs. The method is easily extended to broadband measurements and the simulations show that a bandwidth up to 40% can be handled without problems. Experiments with MagAO-X showed a contrast gain of a factor 10 in a broadband light and a factor 20 to 200 in narrowband light. A contrast of $5\cdot10^{-8}$ was achieved with the Phase Apodized Pupil Lyot Coronagraph at 7.5 $\lambda/D$. The new iEFC method has been demonstrated to work in numerical and lab experiments. It is a method that can be empirically calibrated and it can achieve deep contrast. This makes it a valuable approach for complex ground-based high-contrast imaging systems.Comment: 13 pages, 12 figures accepted by A&

Toward on-sky adaptive optics control using reinforcement learning Model-based policy optimization for adaptive optics

Context. The direct imaging of potentially habitable exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based, extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current control laws of XAO systems leave strong residuals. Aims. Current AO control strategies such as static matrix-based wavefront reconstruction and integrator control suffer from a temporal delay error and are sensitive to mis-registration, that is, to dynamic variations of the control system geometry. We aim to produce control methods that cope with these limitations, provide a significantly improved AO correction, and, therefore, reduce the residual flux in the coronagraphic point spread function (PSF). Methods. We extend previous work in reinforcement learning for AO. The improved method, called the Policy Optimization for Adaptive Optics (PO4AO), learns a dynamics model and optimizes a control neural network, called a policy. We introduce the method and study it through numerical simulations of XAO with Pyramid wavefront sensor (PWFS) for the 8-m and 40-m telescope aperture cases. We further implemented PO4AO and carried out experiments in a laboratory environment using Magellan Adaptive Optics eXtreme system (MagAO-X) at the Steward laboratory. Results. PO4AO provides the desired performance by improving the coronagraphic contrast in numerical simulations by factors of 3-5 within the control region of deformable mirror and PWFS, both in simulation and in the laboratory. The presented method is also quick to train, that is, on timescales of typically 5-10 s, and the inference time is sufficiently small (Peer reviewe

Toward on-sky adaptive optics control using reinforcement learning Model-based policy optimization for adaptive optics

Context. The direct imaging of potentially habitable exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based, extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current control laws of XAO systems leave strong residuals. Aims. Current AO control strategies such as static matrix-based wavefront reconstruction and integrator control suffer from a temporal delay error and are sensitive to mis-registration, that is, to dynamic variations of the control system geometry. We aim to produce control methods that cope with these limitations, provide a significantly improved AO correction, and, therefore, reduce the residual flux in the coronagraphic point spread function (PSF). Methods. We extend previous work in reinforcement learning for AO. The improved method, called the Policy Optimization for Adaptive Optics (PO4AO), learns a dynamics model and optimizes a control neural network, called a policy. We introduce the method and study it through numerical simulations of XAO with Pyramid wavefront sensor (PWFS) for the 8-m and 40-m telescope aperture cases. We further implemented PO4AO and carried out experiments in a laboratory environment using Magellan Adaptive Optics eXtreme system (MagAO-X) at the Steward laboratory. Results. PO4AO provides the desired performance by improving the coronagraphic contrast in numerical simulations by factors of 3-5 within the control region of deformable mirror and PWFS, both in simulation and in the laboratory. The presented method is also quick to train, that is, on timescales of typically 5-10 s, and the inference time is sufficiently small (Peer reviewe

HIP 67506 C: MagAO-X Confirmation of a New Low-Mass Stellar Companion to HIP 67506 A

We report the confirmation of HIP 67506 C, a new stellar companion to HIP 67506 A. We previously reported a candidate signal at 2$\lambda$/D (240~mas) in L$^{\prime}$ in MagAO/Clio imaging using the binary differential imaging technique. Several additional indirect signals showed that the candidate signal merited follow-up: significant astrometric acceleration in Gaia DR3, Hipparcos-Gaia proper motion anomaly, and overluminosity compared to single main sequence stars. We confirmed the companion, HIP 67506 C, at 0.1" with MagAO-X in April, 2022. We characterized HIP 67506 C MagAO-X photometry and astrometry, and estimated spectral type K7-M2; we also re-evaluated HIP 67506 A in light of the close companion. Additionally we show that a previously identified 9" companion, HIP 67506 B, is a much further distant unassociated background star. We also discuss the utility of indirect signposts in identifying small inner working angle candidate companions.Comment: 10 pages, 9 figures, 4 tables, accepted to MNRA

Vector-apodizing phase plate coronagraph: design, current performance, and future development [Invited]

Instrumentatio

Characterizing deformable mirrors for the MagAO-X instrument

The MagAO-X instrument is an extreme adaptive optics system for high-contrast imaging at visible- and near-infrared wavelengths on the Magellan Clay Telescope. A central component of this system is a 2040-actuator microelectromechanical deformable mirror (DM) from Boston Micromachines Corp. that operates at 3.63 kHz for high-order wavefront control (the tweeter). Two additional DMs from ALPAO perform the low-order (the woofer) and non-common-path science-arm wavefront correction (the NCPC DM). Prior to integration with the instrument, we characterized these devices using a Zygo Verifire Interferometer to measure each DM surface. We present the results of the characterization effort here, demonstrating the ability to drive the tweeter to a flat of 6.9 nm root-mean-square (RMS) surface (and 0.56 nm RMS surface within its control bandwidth), the woofer to 2.2-nm RMS surface, and the NCPC DM to 2.1-nm RMS surface over the MagAO-X beam footprint on each device. Using focus-diversity phase retrieval on the MagAO-X science cameras to estimate the internal instrument wavefront error, we further show that the integrated DMs correct the instrument WFE to 18.7 nm RMS, which, combined with a 11.7% pupil amplitude RMS, produces a Strehl ratio of 0.94 at Hα. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Data-driven subspace predictive control of adaptive optics for high-contrast imaging

The search for exoplanets is pushing adaptive optics (AO) 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 AO 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 are 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. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Implicit electric field conjugation: Data-driven focal plane control

Context. Direct imaging of Earth-like planets is one of the main science cases for the next generation of extremely large telescopes. This is very challenging due to the star-planet contrast that has to be overcome. Most current high-contrast imaging instruments are limited in sensitivity at small angular separations due to non-common path aberrations (NCPA). The NCPA leak through the corona-graph and create bright speckles that limit the on-sky contrast and therefore also the post-processed contrast. Aims. We aim to remove the NCPA by active focal plane wavefront control using a data-driven approach. Methods. We developed a new approach to dark hole creation and maintenance that does not require an instrument model. This new approach is called implicit Electric Field Conjugation (iEFC) and it can be empirically calibrated. This makes it robust for complex instruments where optical models might be difficult to realize. Numerical simulations have been used to explore the performance of iEFC for different coronagraphs. The method was validated on the internal source of the Magellan Adaptive Optics extreme (MagAO-X) instrument to demonstrate iEFC’s performance on a real instrument. Results. Numerical experiments demonstrate that iEFC can achieve deep contrast below 10−9 with several coronagraphs. The method is easily extended to broadband measurements and the simulations show that a bandwidth up to 40% can be handled without problems. Lab experiments with MagAO-X showed a contrast gain of a factor 10 in a broadband light and a factor 20–200 in narrowband light. A contrast of 5 × 10−8 was achieved with the Phase Apodized Pupil Lyot Coronagraph at 7.5 λ/D. Conclusions. The new iEFC method has been demonstrated to work in numerical and lab experiments. It is a method that can be empirically calibrated and it can achieve deep contrast. This makes it a valuable approach for complex ground-based high-contrast imaging systems

XPipeline: starlight subtraction at scale for MagAO-X

Instrumentatio

Phasing the Giant Magellan Telescope with the holographic dispersed fringe sensor

The next generation of Giant Segmented Mirror Telescopes (GSMT) will have large gaps between the segments either caused by the shadow of the mechanical structure of the secondary mirror [European Extremely Large Telescope (E-ELT) and Thirty Meter Telescope (TMT)] or intrinsically by design [Giant Magellan Telescope (GMT)]. These gaps are large enough to fragment the aperture into independent segments that are separated by more than the typical Fried parameter. This creates piston and petals modes that are not well sensed by conventional wavefront sensors such as the Shack-Hartmann wavefront sensor or the pyramid wavefront sensor. We propose to use a new optical device, the holographic dispersed fringe sensor (HDFS), to sense and control these petal/piston modes. The HDFS uses a single pupil-plane hologram to interfere the segments onto different spatial locations in the focal plane. Numerical simulations show that the HDFS is very efficient and that it reaches a differential piston root-mean-square (rms) smaller than 10 nm for GMT/E-ELT/TMT for guide stars up to 13th J + H band magnitude. The HDFS has also been validated in the lab with Magellan adaptive optics extreme and high-contrast adaptive optics phasing testbed, the GMT phasing testbed. The lab experiments reached 5-nm rms piston error on the Magellan telescope aperture. The HDFS also reached 50-nm rms of piston error on a segmented GMT-like aperture while the pyramid wavefront sensor was compensating simulated atmosphere under median seeing conditions. The simulations and lab results demonstrate the HDFS as an excellent piston sensor for the GMT. We find that the combination of a pyramid slope sensor with an HDFS piston sensor is a powerful architecture for the GMT. © 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]