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Register a new userXAO-assisted coronagraphy with SHARK NIR: from simulations to laboratory tests
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Added by
Gabriele Umbriaco Dr.
Publication date
2020
fields
Physics
and research's language is
English
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Several Extreme Adaptive Optics (XAO) systems dedicated to the detection and characterisation of the exoplanets are currently in operation for 8-10 meter class telescopes. Coronagraphs are commonly used in these facilities to reject the diffracted light of an observed star and enable direct imaging and spectroscopy of its circumstellar environment. SHARK-NIR is a coronagraphic camera that will be implemented at the Large Binocular Telescope (LBT). After an extensive simulation campaign, SHARK-NIR team selected a suite of coronagraphic techniques to be implemented in the instrument in order to fulfil the scientific requirements. In summary, the Gaussian Lyot coronagraph is the option to serve all those science cases requiring field-stabilization and moderate contrast. Observations in pupil-stabilized mode to search for exoplanets can take advantage of three Shaped Pupil masks (SPC) and a Four-Quadrant Phase Mask (FQPM) coronagraph. The SPC are designed for high contrast on a small field close to the star and are robust to image and pupil jitter. The FQPM allows to access the entire scientific FoV (18x18) and delivers excellent performance in ideal conditions (high Strehl ratios), but performance is still good, both close and further away from the star, even at lower Strehl and with moderate vibrations. After the procurement phase, the coronagraphic masks were delivered to our labs and we started to test their performance on the optical bench and define the alignment procedures that will be employed in the final integration of the instrument in our cleaning room. In this article, we describe the tests that we performed in the lab with SHARK-NIR coronagraphs. We measured the contrast achievable with each technique in very-high Strehl conditions and defined the alignment-integration procedures.
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SHARK-NIR is one of the two coronagraphic instruments proposed for the Large Binocular Telescope. Together with SHARK-VIS (performing coronagraphic imaging in the visible domain), it will offer the possibility to do binocular observations combining direct imaging, coronagraphic imaging and coronagraphic low resolution spectroscopy in a wide wavelength domain, going from 0.5{mu}m to 1.7{mu}m. Additionally, the contemporary usage of LMIRCam, the coronagraphic LBTI NIR camera, working from K to L band, will extend even more the covered wavelength range. In January 2017 SHARK-NIR underwent a successful final design review, which endorsed the instrument for construction and future implementation at LBT. We report here the final design of the instrument, which foresees two intermediate pupil planes and three focal planes to accomodate a certain number of coronagraphic techniques, selected to maximize the instrument contrast at various distances from the star. Exo-Planets search and characterization has been the science case driving the instrument design, but the SOUL upgrade of the LBT AO will increase the instrument performance in the faint end regime, allowing to do galactic (jets and disks) and extra-galactic (AGN and QSO) science on a relatively wide sample of targets, normally not reachable in other similar facilities.
A robust post processing technique is mandatory to analyse the coronagraphic high contrast imaging data. Angular Differential Imaging (ADI) and Principal Component Analysis (PCA) are the most used approaches to suppress the quasi-static structure in the Point Spread Function (PSF) in order to revealing planets at different separations from the host star. The focus of this work is to apply these two data reduction techniques to obtain the best limit detection for each coronagraphic setting that has been simulated for the SHARK-NIR, a coronagraphic camera that will be implemented at the Large Binocular Telescope (LBT). We investigated different seeing conditions ($0.4-1$) for stellar magnitude ranging from R=6 to R=14, with particular care in finding the best compromise between quasi-static speckle subtraction and planet detection.
In a previous paper, we discussed an original solution to improve the performances of coronagraphs by adding, in the optical scheme, an adaptive hologram removing most of the residual speckle starlight. In our simulations, the detection limit in the flux ratio between a host star and a very near planet (5 lambda/D) improves over a factor 1000 (resp. 10000) when equipped with a hologram for cases of wavefront bumpiness imperfections of lambda/20 (resp. lambda/100). We derive, in this paper, the transmission accuracy required on the hologram pixels to achieve such goals. We show that preliminary tests could be performed on the basis of existing technologies.
In this article, we present a simulator conceived for the conceptual study of an AO-fed high-contrast coronagraphic imager. The simulator implements physical optics: a complex disturbance (the electric field) is Fresnel-propagated through any user-defined optical train, in an end-to-end fashion. The effect of atmospheric residual aberrations and their evolution with time can be reproduced by introducing in input a temporal sequence of phase screens: synthetic images are then generated by co-adding instantaneous PSFs. This allows studying with high accuracy the impact of AO correction on image quality for different integration times and observing conditions. In addition, by conveniently detailing the optical model, the user can easily implement any coronagraphic set-up and introduce optical aberrations at any position. Furthermore, generating multiple images can allow exploring detection limits after a differential post-processing algorithm is applied (e.g. Angular Differential Imaging). The simulator has been developed in the framework of the design of SHARK-NIR, the second-generation high contrast imager selected for the Large Binocular Telescope.
The accumulation of aberrations along the optical path in a telescope produces distortions and speckles in the resulting images, limiting the performance of cameras at high angular resolution. It is important to achieve the highest possible sensitivity to faint sources such as planets, using both hardware and data analysis software. While analytic methods are efficient, real systems are better-modelled numerically, but such models with many parameters can be hard to understand, optimize and apply. Automatic differentiation software developed for machine learning now makes calculating derivatives with respect to aberrations straightforward for arbitrary optical systems. We apply this powerful new tool to enhance high-angular-resolution astronomical imaging. Self-calibrating observables such as the closure phase or bispectrum have been widely used in optical and radio astronomy to mitigate optical aberrations and achieve high-fidelity imagery. Kernel phases are a generalization of closure phases in the limit of small phase errors. Using automatic differentiation, we reproduce existing kernel phase theory within this framework and demonstrate an extension to the Lyot coronagraph, finding self-calibrating combinations of speckles which are resistant to phase noise, but only in the very high-wavefront-quality regime. As an illustrative example, we reanalyze Palomar adaptive optics observations of the binary alpha Ophiuchi, finding consistency between the new pipeline and the existing standard. We present a new Python package morphine that incorporates these ideas, with an interface similar to the popular package poppy, for optical simulation with automatic differentiation. These methods may be useful for designing improved astronomical optical systems by gradient descent.
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