Joint optimization of wavefront sensing and reconstruction with automatic differentiation

Abstract

High-contrast imaging instruments need extreme wavefront control to directly image exoplanets. This requires highly sensitive wavefront sensors which optimally make use of the available photons to sense the wavefront. Here, we propose to numerically optimize Fourier-filtering wavefront sensors using automatic differentiation. First, we optimize the sensitivity of the wavefront sensor for different apertures and wavefront distributions. We find sensors that are more sensitive than currently used sensors and close to the theoretical limit, under the assumption of monochromatic light. Subsequently, we directly minimize the residual wavefront error by jointly optimizing the sensing and reconstruction. This is done by connecting differentiable models of the wavefront sensor and reconstructor and alternatingly improving them using a gradient-based optimizer. We also allow for nonlinearities in the wavefront reconstruction using Convolutional Neural Networks, which extends the design space of the wavefront sensor. Our results show that optimization can lead to wavefront sensors that have improved performance over currently used wavefront sensors. The proposed approach is flexible, and can in principle be used for any wavefront sensor architecture with free design parameters.

Nonlinear Wave Front Reconstruction from a Pyramid Sensor using Neural Networks

“…In a tangentially related and rapidly growing area of research, automatic differentiation has proven to be a powerful tool for the optimization of optical systems (Pope et al 2021;). This has since been used to jointly optimize the sensitivity of Fourier-filtering wave front sensors and their reconstructor and are able to achieve greater sensitivity compared to the PyWFS (Landman et al 2022). Reinforcement learning is also surfacing as a promising tool in AO control, which could be used in conjunction with a PyWFS (Landman et al 2021).…”