High-order coronagraphic wavefront control with algorithmic differentiation: first experimental demonstration

Future space-based coronagraphs will rely critically on focal-plane wavefront sensing and control with deformable mirrors (DMs) to reach deep contrast by mitigating optical aberrations in the primary beam path. Until now, most focal-plane wavefront control algorithms have been formulated in terms of Jacobian matrices, which encode the predicted effect of each DM actuator on the focal-plane electric field. A disadvantage of these methods is that Jacobian matrices can be cumbersome to compute and manipulate, particularly when the number of DM actuators is large. Recently, we proposed a new class of focal-plane wavefront control algorithms that utilize gradient-based optimization with algorithmic differentiation to compute wavefront control solutions while avoiding the explicit computation and manipulation of Jacobian matrices entirely. In simulations using a coronagraph design for the proposed Large UV/Optical/Infrared Surveyor, we showed that our approach reduces overall CPU time and memory consumption compared to a Jacobian-based algorithm. Here, we expand on these results by implementing the proposed algorithm on the High-contrast Imager for Complex Aperture Telescopes tested at the Space Telescope Science Institute and present initial experimental results, demonstrating contrast suppression capabilities equivalent to Jacobian-based methods.