Focal Plane Wavefront Control using Reinforcement Learning for High-Contrast Imaging

High Contrast Imaging (HCI) on ground-based telescopes suffer from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are adept at correcting these aberrations, but fall short in the correction of non-common path aberrations (NCPAs). NCPAs arise because the wavefront sensor (WFS) measures and corrects a wavefront that is different from that affecting the science images, thus requiring additional intervention. This presentation focuses on real-time training and correction of NCPAs using reinforcement learning, particularly the correction of water vapor seeing experienced by METIS.