On-sky results of the Leiden EXoplanet Instrument (LEXI)

Haffert, Sebastiaan Y.; Keller, Christoph U.; Snellen, I. A. G.; Por, Emiel H.; Doelman, David; Wardenier, Joost P.; Wilby, Michael J.; Kooten, Maaike A. M. van; Bos, Steven P.

doi:10.1117/12.2314199

Cited by 6 publications

(12 citation statements)

References 9 publications

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“…The lab setup uses the AO system of the exoplanet path-finder instrument LEXI [22], which uses a g-ODWFS as its main wavefront sensor. To generate a diffraction-limited input beam a 633nm HeNe laser is injected into a single-mode fiber.…”

Section: Methodsmentioning

confidence: 99%

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Nonlinear wavefront reconstruction with convolutional neural networks for Fourier-based wavefront sensors

Landman¹,

Haffert²

2020

Opt. Express

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Fourier-based wavefront sensors, such as the Pyramid Wavefront Sensor (PWFS), are the current preference for high contrast imaging due to their high sensitivity. However, these wavefront sensors have intrinsic nonlinearities that constrain the range where conventional linear reconstruction methods can be used to accurately estimate the incoming wavefront aberrations. We propose to use Convolutional Neural Networks (CNNs) for the nonlinear reconstruction of the wavefront sensor measurements. It is demonstrated that a CNN can be used to accurately reconstruct the nonlinearities in both simulations and a lab implementation. We show that solely using a CNN for the reconstruction leads to suboptimal closed loop performance under simulated atmospheric turbulence. However, it is demonstrated that using a CNN to estimate the nonlinear error term on top of a linear model results in an improved effective dynamic range of a simulated adaptive optics system. The larger effective dynamic range results in a higher Strehl ratio under conditions where the nonlinear error is relevant. This will allow the current and future generation of large astronomical telescopes to work in a wider range of atmospheric conditions and therefore reduce costly downtime of such facilities.

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Section: Methodsmentioning

confidence: 99%

“…Section 3 provides a comparison between the reconstruction methods in simulations to show the potential gain in an idealised system. Section 4 discusses the results from applying the CNN in a lab setup of the Leiden EXoplanet Instrument (LEXI) [22] that has an implementation of the g-ODWFS as its main wavefront sensor.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear wavefront reconstruction with convolutional neural networks for Fourier-based wavefront sensors

Landman¹,

Haffert²

2020

Opt. Express

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“…FPWFS is critical to remove non-common path aberrations (NCPA), aberrations from optics after the AO system, which are therefore unseen by the AO wavefront sensor. An example of a focal-plane wavefront sensor is the coronagraphic modal wavefront sensor (cMWS) [23,38], which provides simultaneous coronagraphic imaging and focal-plane wavefront sensing with the science point-spread function. The cMWS creates multiple holograms, each biased with a different wavefront mode drawn from a suitable basis set.…”

Section: Focal-plane Wavefront Sensingmentioning

confidence: 99%

“…The trade-off between modal coverage, sensitivity and Strehl is difficult to optimize, as it is a priori unclear how much the selected wavefront modes contribute to speckles. The cMWS has successfully been tested with the Leiden exoplanet instrument (LEXI) [23], and in lab demonstrations of Magellan/MagAO-X [42]. Another method for FPWFS is an adaptation of phase diversity (PD) [43].…”

Section: Focal-plane Wavefront Sensingmentioning

confidence: 99%

“…Moreover, the direct-write system is capable of writing much finer structures (∼ 1 m) compared to diamond turning (∼ 50 m) and has the ability to write discontinuous phase steps. These properties enable a tremendously increased phase pattern complexity that can be used for holography [23] and manufacture more optimal phase patterns [24]. A third kind of vAPP is the double-grating vAPP (dgvAPP).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Over the last decade, the vector-apodizing phase plate (vAPP) coronagraph has been developed from concept to on-sky application in many high-contrast imaging systems on 8-m class telescopes. The vAPP is an geometric-phase patterned coronagraph that is inherently broadband, and its manufacturing is enabled only by direct-write technology for liquid-crystal patterns. The vAPP generates two coronagraphic PSFs that cancel starlight on opposite sides of the point spread function (PSF) and have opposite circular polarization states. The efficiency, that is the amount of light in these PSFs, depends on the retardance offset from half-wave of the liquid-crystal retarder. Using different liquid-crystal recipes to tune the retardance, different vAPPs operate with high efficiencies (> 96%) in the visible and thermal infrared (0.55 µm to 5 µm). Since 2015, seven vAPPs have been installed in a total of six different instruments, including Magellan/MagAO, Magellan/MagAO-X, Subaru/SCExAO, and LBT/LMIRcam. Using two integral field spectrographs installed on the latter two instruments, these vAPPs can provide lowresolution spectra (R∼30) between 1 m and 5 m. We review the design process, development, commissioning, on-sky performance, and first scientific results of all commissioned vAPPs. We report on the lessons learned and conclude with perspectives for future developments and applications.

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