The leading difficulty in achieving the contrast necessary to directly image exoplanets and associated structures (e.g., protoplanetary disks) at wavelengths ranging from the visible to the infrared is quasistatic speckles (QSSs). QSSs are hard to distinguish from planets at the necessary level of precision to achieve high contrast. QSSs are the result of hardware aberrations that are not compensated for by the adaptive optics (AO) system; these aberrations are called noncommon path aberrations (NCPAs). In 2013, Frazin showed how simultaneous millisecond telemetry from the wavefront sensor (WFS) and a science camera behind a stellar coronagraph can be used as input into a regression scheme that simultaneously and selfconsistently estimates NCPAs and the soughtafter image of the planetary system (
 Award ID(s):
 1710514
 NSFPAR ID:
 10073977
 Date Published:
 Journal Name:
 Proceedings of the SPIE
 Volume:
 10703
 Page Range / eLocation ID:
 107032N
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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exoplanet image). When run in a closedloop configuration, the WFS measures the corrected wavefront, called theAO residual (AOR)wavefront . The physical principle underlying the regression method is rather simple: when an image is formed at the science camera, the AOR modules both the speckles arising from NCPAs as well as the planetary image. Therefore, the AOR can be used as a probe to estimate NCPA and the exoplanet image via regression techniques. The regression approach is made more difficult by the fact that the AOR is not exactly known since it can be estimated only from the WFS telemetry. The simulations in the Part I paper provide results on the joint regression on NCPAs and the exoplanet image from three different methods, calledideal ,naïve , andbiascorrected estimators. The ideal estimator is not physically realizable (it is useful as a benchmark for simulation studies), but the other two are. The ideal estimator uses true AOR values (available in simulation studies), but it treats the noise in focal plane images via standard linearized regression. Naïve regression uses the same regression equations as the ideal estimator, except that it substitutes the estimated values of the AOR for true AOR values in the regression formulas, which can result in problematic biases (however, Part I provides an example in which the naïve estimate makes a useful estimate of NCPAs). The biascorrected estimator treats the errors in AOR estimates, but it requires the probability distribution that governs the errors in AOR estimates. This paper provides the regression equations for ideal, naïve, and biascorrected estimators, as well as a supporting technical discussion. 
One of the top priorities in observational astronomy is the direct imaging and characterization of extrasolar planets (exoplanets) and planetary systems. Direct images of rocky exoplanets are of particular interest in the search for life beyond the Earth, but they tend to be rather challenging targets since they are ordersofmagnitude dimmer than their host stars and are separated by small angular distances that are comparable to the classical
$\mathrm{\lambda <\#comment/>}/D$ diffraction limit, even for the coming generation of 30 m class telescopes. Current and planned efforts for groundbased direct imaging of exoplanets combine highorder adaptive optics (AO) with a stellar coronagraph observing at wavelengths ranging from the visible to the midIR. The primary barrier to achieving high contrast with current direct imaging methods is quasistatic speckles, caused largely by noncommon path aberrations (NCPAs) in the coronagraph optical train. Recent work has demonstrated that millisecond imaging, which effectively “freezes” the atmosphere’s turbulent phase screens, should allow the wavefront sensor (WFS) telemetry to be used as a probe of the optical system to measure NCPAs. Starting with a realistic model of a telescope with an AO system and a stellar coronagraph, this paper provides simulations of several closely related regression models that take advantage of millisecond telemetry from the WFS and coronagraph’s science camera. The simplest regression model, called the naïve estimator, does not treat the noise and other sources of information loss in the WFS. Despite its flaws, in one of the simulations presented herein, the naïve estimator provides a useful estimate of an NCPA of$\sim <\#comment/>0.5$ radian RMS ($\approx <\#comment/>\mathrm{\lambda <\#comment/>}/13$ ), with an accuracy of$\sim <\#comment/>0.06$ radian RMS in 1 min of simulated sky time on a magnitude 8 star. Thebiascorrected estimator generalizes the regression model to account for the noise and information loss in the WFS. A simulation of the biascorrected estimator with 4 min of sky time included an NCPA of$\sim <\#comment/>0.05$ radian RMS ($\approx <\#comment/>\mathrm{\lambda <\#comment/>}/130$ ) and an extended exoplanet scene. The joint regression of the biascorrected estimator simultaneously achieved an NCPA estimate with an accuracy of$\sim <\#comment/>5\times <\#comment/>{10}^{<\#comment/>3}$ radian RMS and an estimate of the exoplanet scene that was free of the selfsubtraction artifacts typically associated with differential imaging. The$5\mathrm{\sigma <\#comment/>}$ contrast achieved by imaging of the exoplanet scene was$\sim <\#comment/>1.7\times <\#comment/>{10}^{<\#comment/>4}$ at a distance of$3\mathrm{\lambda <\#comment/>}/D$ from the star and$\sim <\#comment/>2.1\times <\#comment/>{10}^{<\#comment/>5}$ at$10\mathrm{\lambda <\#comment/>}/D$ . These contrast values are comparable to the very best onsky results obtained from multiwavelength observations that employ both angular differential imaging (ADI) and spectral differential imaging (SDI). This comparable performance is despite the fact that our simulations are quasimonochromatic, which makes SDI impossible, nor do they have diurnal field rotation, which makes ADI impossible. The error covariance matrix of the joint regression shows substantial correlations in the exoplanet and NCPA estimation errors, indicating that exoplanet intensity and NCPA need to be estimated selfconsistently to achieve high contrast. 
null (Ed.)A closedloop control algorithm for the reduction of turbulent flow separation over NACA 0015 airfoil equipped with leadingedge synthetic jet actuators (SJAs) is presented. A system identification approach based on Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX) technique was used to predict nonlinear dynamics of the fluid flow and for the design of the controller system. Numerical simulations based on URANS equations are performed at Reynolds number of 106 for various airfoil incidences with and without closedloop control. The NARMAX model for flow over an airfoil is based on the static pressure data, and the synthetic jet actuator is developed using an incompressible flow model. The corresponding NARMAX identification model developed for the pressure data is nonlinear; therefore, the describing function technique is used to linearize the system within its frequency range. Lowpass filtering is used to obtain quasilinear state values, which assist in the application of linear control techniques. The reference signal signifies the condition of a fully reattached flow, and it is determined based on the linearization of the original signal during openloop control. The controller design follows the standard proportionalintegral (PI) technique for the singleinput singleoutput system. The resulting closedloop response tracks the reference value and leads to significant improvements in the transient response over the openloop system. The NARMAX controller enhances the lift coefficient from 0.787 for the uncontrolled case to 1.315 for the controlled case with an increase of 67.1%.more » « less

Abstract We present a novel machinelearning approach for detecting faint point sources in highcontrast adaptive optics (AO) imaging data sets. The most widely used algorithms for primary subtraction aim to decouple bright stellar speckle noise from planetary signatures by subtracting an approximation of the temporally evolving stellar noise from each frame in an imaging sequence. Our approach aims to improve the stellar noise approximation and increase the planet detection sensitivity by leveraging deep learning in a novel direct imaging postprocessing algorithm. We show that a convolutional autoencoder neural network, trained on an extensive reference library of real imaging sequences, accurately reconstructs the stellar speckle noise at the location of a potential planet signal. This tool is used in a postprocessing algorithm we call Direct Exoplanet Detection with Convolutional Image Reconstruction, or
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