6 papers · 1 filter
Exploring reinforcement learning to enhance focal-plane wavefront control for vortex coronagraphs
Iremsu Taskin, Jalo Nousiainen, Gilles Orban de Xivry +2
High Contrast Imaging (HCI) on ground-based telescopes suffers from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are a…
On-sky demonstration of reinforcement learning for adaptive optics control
Jalo Nousiainen, Vincent Chambouleyron, Benoit Neichel +7
Reinforcement learning (RL)-based algorithms have recently emerged as a promising approach for adaptive optics (AO) control. In simulations and laboratory experiments, they have de…
Focal plane wavefront control with model-based reinforcement learning
Jalo Nousiainen, Iremsu Taskin, Markus Kasper +2
The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging instruments on extremely large telescopes. Most such exoplanets orbit clo…
The GPU-based High-order adaptive OpticS Testbench
Byron Engler, Markus Kasper, Serban Leveratto +12
The GPU-based High-order adaptive OpticS Testbench (GHOST) at the European Southern Observatory (ESO) is a new 2-stage extreme adaptive optics (XAO) testbench at ESO. The GHOST is…
The power of prediction: spatiotemporal Gaussian process modeling for predictive control in slope-based wavefront sensing
Jalo Nousiainen, Juha-Pekka Puska, Tapio Helin +2
Time-delay error is a significant error source in adaptive optics (AO) systems. It arises from the latency between sensing the wavefront and applying the correction. Predictive con…
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control
Jalo Nousiainen, Byron Engler, Markus Kasper +5
Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are loca…