23 research outputs found
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 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 was achieved with the Phase
Apodized Pupil Lyot Coronagraph at 7.5 . 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/D (240~mas) in
L 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
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Spatial linear dark field control and holographic modal wavefront sensing with a vAPP coronagraph on MagAO-X
The Magellan Extreme Adaptive Optics (MagAO-X) Instrument is an extreme AO system coming online at the end of 2019 that will be operating within the visible and near-IR. With state-of-the-art wavefront sensing and coronagraphy, MagAO-X will be optimized for high-contrast direct exoplanet imaging at challenging visible wavelengths, particularly Hα. To enable high-contrast imaging, the instrument hosts a vector apodizing phase plate (vAPP) coronagraph. The vAPP creates a static region of high contrast next to the star that is referred to as a dark hole; on MagAO-X, the expected dark hole raw contrast is ∼4 × 10 − 6. The ability to maintain this contrast during observations, however, is limited by the presence of non-common path aberrations (NCPA) and the resulting quasi-static speckles that remain unsensed and uncorrected by the primary AO system. These quasi-static speckles within the dark hole degrade the high contrast achieved by the vAPP and dominate the light from an exoplanet. The aim of our efforts here is to demonstrate two focal plane wavefront sensing (FPWFS) techniques for sensing NCPA and suppressing quasi-static speckles in the final focal plane. To sense NCPA to which the primary AO system is blind, the science image is used as a secondary wavefront sensor. With the vAPP, a static high-contrast dark hole is created on one side of the PSF, leaving the opposite side of the PSF unocculted. In this unobscured region, referred to as the bright field, the relationship between modulations in intensity and low-amplitude pupil plane phase aberrations can be approximated as linear. The bright field can therefore be used as a linear wavefront sensor to detect small NCPA and suppress quasi-static speckles. This technique, known as spatial linear dark field control (LDFC), can monitor the bright field for aberrations that will degrade the high-contrast dark hole. A second form of FPWFS, known as holographic modal wavefront sensing (hMWFS), is also employed with the vAPP. This technique uses hologram-generated PSFs in the science image to monitor the presence of low-order aberrations. With LDFC and the hMWFS, high contrast across the dark hole can be maintained over long observations, thereby allowing planet light to remain visible above the stellar noise over the course of observations on MagAO-X. Here, we present simulations and laboratory demonstrations of both spatial LDFC and the hMWFS with a vAPP coronagraph at the University of Arizona Extreme Wavefront Control Laboratory. We show both in simulation and in the lab that the hMWFS can be used to sense low-order aberrations and reduce the wavefront error (WFE) by a factor of 3 − 4 × . We also show in simulation that, in the presence of a temporally evolving pupil plane phase aberration with 27-nm root-mean-square (RMS) WFE, LDFC can reduce the WFE to 18-nm RMS, resulting in factor of 6 to 10 gain in contrast that is kept stable over time. This performance is also verified in the lab, showing that LDFC is capable of returning the dark hole to the average contrast expected under ideal lab conditions. These results demonstrate the power of the hMWFS and spatial LDFC to improve MagAO-X’s high-contrast imaging capabilities for direct exoplanet imaging.Instrumentatio
Vector-apodizing phase plate coronagraph: design, current performance, and future development [Invited]
Instrumentatio
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
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]