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

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 a 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 point spread functions (PSFs) that cancel starlight on opposite sides of the PSF and have opposite circular polarization states. The efficiency, that is, the amount of light in these PSFs, depends on the retardance offset from a half-wave of the liquid-crystal retarder. Using different liquid-crystal recipes to tune the retardance, different vAPPs operate with high efficiencies (${\gt}96\%$) 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 low-resolution spectra (${\rm{R}} \sim 30$) between 1 µm and 5 µm. 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.

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

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.

Highly multiplexed Bragg gratings for large field of view gas sensing in planetary atmospheres.

Detecting and monitoring gas species is an important part of remote sensing because the state of the environment can be retrieved from the state of the gas species. This can be used to track temperature and pressure structures in the atmosphere for weather predictions, or monitor the air quality. Discriminating different species is easier at higher spectral resolution when the spectral lines are clearly resolved. The need to do this at high spatial resolution and over large fields of view leads to a trade-off between spectral and spatial resolution and spectral bandwidth. We propose to use a highly multiplexed Bragg grating that can optically combine the relevant information from the spectrum without the need to disperse the whole spectrum. This allows us to circumvent the spatial and spectral trade-off and therefore substantially increase the field of view compared to conventional hyperspectral imagers. A dynamic implementation based on acousto-optical filters that can be adapted on the fly is discussed as an easy and flexible way to create the multiplexed gratings. We describe the details of multiplexed Bragg gratings and show that we can retrieve the spatial distribution of individual species abundances in gas mixtures, and we show that we can even do this for the atmospheres of exoplanets orbiting far-away stars.

Generalised optical differentiation wavefront sensor: a sensitive high dynamic range wavefront sensor.

Current wavefront sensors for high resolution imaging have either a large dynamic range or a high sensitivity. A new kind of wavefront sensor is developed which can have both: the Generalised Optical Differentiation wavefront sensor. This new wavefront sensor is based on the principles of optical differentiation by amplitude filters. We have extended the theory behind linear optical differentiation and generalised it to nonlinear filters. We used numerical simulations and laboratory experiments to investigate the properties of the generalised wavefront sensor. With this we created a new filter that can decouple the dynamic range from the sensitivity. These properties make it suitable for adaptive optic systems where a large range of phase aberrations have to be measured with high precision.