Inverse design and optimization of an aperiodic multi-notch fiber Bragg grating using neural networks.

Recent developments in the application of aperiodic fiber Bragg gratings (AFBGs) in astrophotonics, such as AFBG for astronomical near-infrared OH suppression and gas detection based on cross-correlation spectroscopy, have illuminated the problem that the optimization for AFBG with certain fabrication constraints has not been fully investigated and solved. Previous solutions will either sacrifice part of the spectral features or consume a significant amount of computation resources and time. Inspired by recently successful applications of artificial neural networks (ANNs) in photonics inverse design, we develop an AFBG optimization approach employing ANNs in conjunction with genetic algorithms (GAs) for the first time, to the best of our knowledge. The approach maintains the spectral notch depths and preserves the fourth-order super-Gaussian spectral features with improvements of interline loss by ∼100 times. We also implement, to our knowledge, the first inverse scattering neural network based on a tandem architecture for AFBG, using a first-order Gaussian notch profile. The neural network successfully converges but has a poor predictive capability for the phase part of the design. We discuss possible ways to overcome these limitations.

Building hybridized 28-baseline pupil-remapping photonic interferometers for future high-resolution imaging.

One key advantage of single-mode photonic technologies for interferometric use is their ability to easily scale to an ever-increasing number of inputs without a major increase in the overall device size, compared to traditional bulk optics. This is particularly important for the upcoming extremely large telescope (ELT) generation of telescopes currently under construction. We demonstrate the fabrication and characterization of a hybridized photonic interferometer, with eight simultaneous inputs, forming 28 baselines, which is the largest amount to date, to the best of our knowledge. Using different photonic fabrication technologies, we combine a 3D pupil remapper with a planar eight-port ABCD pairwise beam combiner, along with the injection optics necessary for telescope use, into a single integrated monolithic device. We successfully realized a combined device called Dragonfly, which demonstrates a raw instrumental closure-phase stability down to 0.9° over $8\pi$ phase piston error, relating to a detection contrast of ${\sim}6.5 \times {10^{- 4}}$ on an adaptive-optics-corrected 8 m telescope. This prototype successfully demonstrates advanced hybridization and packaging techniques necessary for on-sky use for high-contrast detection at small inner working angles, ideally complementing what can currently be achieved using coronagraphs.

An all-photonic focal-plane wavefront sensor.

Adaptive optics (AO) is critical in astronomy, optical communications and remote sensing to deal with the rapid blurring caused by the Earth's turbulent atmosphere. But current AO systems are limited by their wavefront sensors, which need to be in an optical plane non-common to the science image and are insensitive to certain wavefront-error modes. Here we present a wavefront sensor based on a photonic lantern fibre-mode-converter and deep learning, which can be placed at the same focal plane as the science image, and is optimal for single-mode fibre injection. By measuring the intensities of an array of single-mode outputs, both phase and amplitude information on the incident wavefront can be reconstructed. We demonstrate the concept with simulations and an experimental realisation wherein Zernike wavefront errors are recovered from focal-plane measurements to a precision of 5.1 × 10-3 π radians root-mean-squared-error.

A close halo of large transparent grains around extreme red giant stars.

An intermediate-mass star ends its life by ejecting the bulk of its envelope in a slow, dense wind. Stellar pulsations are thought to elevate gas to an altitude cool enough for the condensation of dust, which is then accelerated by radiation pressure, entraining the gas and driving the wind. Explaining the amount of mass loss, however, has been a problem because of the difficulty of observing tenuous gas and dust only tens of milliarcseconds from the star. For this reason, there is no consensus on the way sufficient momentum is transferred from the light from the star to the outflow. Here we report spatially resolved, multiwavelength observations of circumstellar dust shells of three stars on the asymptotic giant branch of the Hertzsprung-Russell diagram. When imaged in scattered light, dust shells were found at remarkably small radii (less than about two stellar radii) and with unexpectedly large grains (about 300 nanometres in radius). This proximity to the photosphere argues for dust species that are transparent to the light from the star and, therefore, resistant to sublimation by the intense radiation field. Although transparency usually implies insufficient radiative pressure to drive a wind, the radiation field can accelerate these large grains through photon scattering rather than absorption--a plausible mass loss mechanism for lower-amplitude pulsating stars.