Apodized Pupil Lyot Coronagraphs for Arbitrary Apertures. IV. Reduced Inner Working Angle and Increased Robustness to Low-Order Aberrations

The Apodized Pupil Lyot Coronagraph (APLC) is a diffraction suppression system installed in the recently deployed instruments Palomar/P1640, Gemini/GPI, and VLT/SPHERE to allow direct imaging and spectroscopy of circumstellar environments. Using a prolate apodization, the current implementations offer raw contrasts down to $10^{-7}$ at 0.2 arcsec from a star over a wide bandpass (20\%), in the presence of central obstruction and struts, enabling the study of young or massive gaseous planets. Observations of older or lighter companions at smaller separations would require improvements in terms of inner working angle (IWA) and contrast, but the methods originally used for these designs were not able to fully explore the parameter space. We here propose a novel approach to improve the APLC performance. Our method relies on the linear properties of the coronagraphic electric field with the apodization at any wavelength to develop numerical solutions producing coronagraphic star images with high-contrast region in broadband light. We explore the parameter space by considering different aperture geometries, contrast levels, dark-zone sizes, bandpasses, and focal plane mask sizes. We present an application of these solutions to the case of Gemini/GPI with a design delivering a $10^{-8}$ raw contrast at 0.19 arcsec and offering a significantly reduced sensitivity to low-order aberrations compared to the current implementation. Optimal solutions have also been found to reach $10^{-10}$ contrast in broadband light regardless of the telescope aperture shape (in particular the central obstruction size), with effective IWA in the $2-3.5\lambda/D$ range, therefore making the APLC a suitable option for the future exoplanet direct imagers on the ground or in space.Comment: 14 pages, 10 figures, accepted in Ap

Calibration of quasi-static aberrations in exoplanet direct-imaging instruments with a Zernike phase-mask sensor

Context. Several exoplanet direct imaging instruments will soon be in operation. They use an extreme adaptive optics (XAO) system to correct the atmospheric turbulence and provide a highly-corrected beam to a near-infrared (NIR) coronagraph for starlight suppression. The performance of the coronagraph is however limited by the non-common path aberrations (NCPA) due to the differential wavefront errors existing between the visible XAO sensing path and the NIR science path, leading to residual speckles in the coronagraphic image. Aims. Several approaches have been developed in the past few years to accurately calibrate the NCPA, correct the quasi-static speckles and allow the observation of exoplanets at least 1e6 fainter than their host star. We propose an approach based on the Zernike phase-contrast method for the measurements of the NCPA between the optical path seen by the visible XAO wavefront sensor and that seen by the near-IR coronagraph. Methods. This approach uses a focal plane phase mask of size {\lambda}/D, where {\lambda} and D denote the wavelength and the telescope aperture diameter, respectively, to measure the quasi-static aberrations in the upstream pupil plane by encoding them into intensity variations in the downstream pupil image. We develop a rigorous formalism, leading to highly accurate measurement of the NCPA, in a quasi-linear way during the observation. Results. For a static phase map of standard deviation 44 nm rms at {\lambda} = 1.625 {\mu}m (0.026 {\lambda}), we estimate a possible reduction of the chromatic NCPA by a factor ranging from 3 to 10 in the presence of AO residuals compared with the expected performance of a typical current-generation system. This would allow a reduction of the level of quasi-static speckles in the detected images by a factor 10 to 100 hence, correspondingly improving the capacity to observe exoplanets.Comment: 11 pages, 14 figures, A&A accepted, 2nd version after language-editor correction

k-nearest neighbors prediction and classification for spatial data

We propose a nonparametric predictor and a supervised classification based on the regression function estimate of a spatial real variable using k-nearest neighbors method (k-NN). Under some assumptions, we establish almost complete or sure convergence of the proposed estimates which incorporate a spatial proximity between observations. Numerical results on simulated and real fish data illustrate the behavior of the given predictor and classification method