Computationally efficient adaptive wavefront control for coronagraphy using algorithmic differentiation

Wavefront sensing and control using deformable mirrors is a critical technology for achieving direct imaging of Earth-like exoplanets and circumstellar dust clouds using future space-based observatories, a key scientific target of the primary mission recommended by the Astro2020 Decadal Survey. In recent work, Will, Groff and Fienup (JATIS, 2021) showed how formulating the focal-plane wavefront control problem as a nonlinear optimization problem, and computing the necessary derivatives using algorithmic differentiation, enables deformable mirror commands to be obtained without computing a control Jacobian matrix. This greatly reduces the computational resources (CPU time and memory) compared to conventional methods. Later, Will et al. (Proc. SPIE, 2021) demonstrated this approach experimentally using the High Contrast Imager for Complex Aperture Telescopes (HiCAT) coronagraphy testbed, showing equivalent performance in coronagraph contrast to current Jacobian-based approaches. We propose to extend on this work by implementing and experimentally testing adaptive control capabilities, in which experimental data is used to improve the performance of the control loop by tuning the computer model used by the control algorithm to better reflect reality. Our approach will build upon our Jacobian-free control framework to bring its computational benefits to existing Jacobian-based adaptive control methods (Sun et al., JATIS, 2018), which have been shown to improve the convergence rate of the control loop. This, in turn, reduces the overheads associated with wavefront sensing and control, leaving more time for scientific observations and ultimately increasing scientific yield.

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