The reliability of Free Space Optical (FSO) communications between a ground station and celestial objects is significantly hampered by the variability in atmospheric conditions. Enhancing the system’s capabilities to recover the received signal can significantly increase the robustness and broaden the operational scope of this type of communication. One of the most promising avenues for improvement entails integrating Adaptive Optics systems with the latest Machine Learning techniques. We study different control laws based on a classical integrator, a LQG with a Kalman filter (with a second order autoregressive model) and a Reinforcement Learning approach: we evaluate the performance of the three control laws with the Strehl ratio.
The rise of exoplanet research and the growing need for high-capacity free-space optical communication links have placed high demands on the performance of adaptive optics (AO) systems. A key challenge lies in mitigating temporal error, this imposes severe time constraints on the response of deformable mirrors (DM). Recent advancements at ALPAO in the realm of input shaping techniques have yielded promising results, effectively eliminating oscillations and reducing overshoot from 60 to less than 5%. Both, rise time and settling time have been diminished to below 50μs, representing an improvement of one order of magnitude compared to the unshaped case. The solution found is compatible with real-time computing constraints and can be integrated in the DM drive electronics or in separate processing unit.
Direct imaging instruments have the spatial resolution to resolve exoplanets from their host star. This enables direct characterization of the exoplanets atmosphere, but most direct imaging instruments do not have spectrographs with high enough resolving power for detailed atmospheric characterization. We investigate the use of a single-mode diffraction-limited integral-field unit that is compact and easy to integrate into current and future direct imaging instruments for exoplanet characterization. This achieved by making use of recent progress in photonic manufacturing to create a single-mode fiber-fed image reformatter. The fiber link is created with three-dimensional printed lenses on top of a single-mode multicore fiber that feeds an ultrafast laser inscribed photonic chip that reformats the fiber into a pseudoslit. We then couple it to a first-order spectrograph with a triple stacked volume phase holographic grating for a high efficiency over a large bandwidth. The prototype system has had a successful first-light observing run at the 4.2-m William Herschel Telescope. The measured on-sky resolving power is between 2500 and 3000, depending on the wavelength. With our observations, we show that single-mode integral-field spectroscopy is a viable option for current and future exoplanet imaging instruments.
The Multi-Core Integral-Field Unit (MCIFU) is a new diffraction-limited near-infrared integral-field unit for exoplanet atmosphere characterization with extreme adaptive optics (xAO) instruments. It has been developed as an experimental pathfinder for spectroscopic upgrades for SPHERE+/VLT and other xAO systems. The wavelength range covers 1.0 um to 1.6um at a resolving power around 5000 for 73 points on-sky. The MCIFU uses novel astrophotonic components to make this very compact and robust spectrograph. We performed the first successful on-sky test with CANARY at the 4.2 meter William Herschel Telescope in July 2019, where observed standard stars and several stellar binaries. An improved version of the MCIFU will be used with MagAO-X, the new extreme adaptive optics system at the 6.5 meter Magellan Clay telescope in Chile. We will show and discuss the first-light performance and operations of the MCIFU at CANARY and discuss the integration of the MCIFU with MagAO-X.
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