Focal plane wavefront control with model-based reinforcement learning

Source abstract

Context . The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging (HCI) instruments on ground-based, extremely large telescopes. Most such exoplanets orbit close to their host stars, where their observation is limited by fast-moving atmospheric speckles and quasi-static noncommon path aberrations (NCPA). Aims . Conventional NCPA correction methods often use mechanical mirror probes, which compromise performance during operation. This work presents machine-learning-based NCPA control methods that automatically detect and correct both dynamic and static NCPA errors by leveraging past telemetry data and sequential phase diversity. Methods . We extend previous work in reinforcement learning (RL) for adaptive optics (AO) to focal plane wavefront control. A new model-based RL algorithm, Policy Optimization for Noncommon Path Aberrations (PO4NCPA), interprets the focal plane image as input data and, through sequential phase diversity, determines phase corrections that optimize both non-coronagraphic and post-coronagraphic point spread functions (PSFs) without prior system knowledge. Furthermore, we demonstrate the effectiveness of this approach by numerically simulating static NCPA errors on a ground-based telescope and an infrared imager affected by water vapor-induced seeing (dynamic NCPAs). Results . Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamic NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator in these metrics, though with a higher wavefront root mean square error (RMSE), especially for high-order modes. The method remains effective for the Extremely Large Telescope (ELT) pupil, the vector vortex coronagraph, under photon and background noise. Conclusions . PO4NCPA is model-free and can be directly applied to standard imaging as well as to any type of coronagraphy; its submillisecond inference times and performance also make it suitable for real-time low-order correction of atmospheric turbulence beyond HCI requirements.