Abstract

The pyramid wavefront sensor (PWFS) can provide the sensitivity needed for demanding adaptive optics applications, such as imaging exoplanets using the future extremely large telescopes of over 30 m of diameter (D). However, its exquisite sensitivity has a limited linear range of operation, or dynamic range, although it can be extended through the use of beam modulation-despite sacrificing sensitivity and requiring additional optical hardware. Inspired by artificial intelligence techniques, this work proposes to train an optical layer-comprising a passive diffractive element placed at a conjugated Fourier plane of the pyramid prism-to boost the linear response of the pyramid sensor without the need for cumbersome modulation. We develop an end-2-end simulation to train the diffractive element, which acts as an optical preconditioner to the traditional least-square modal phase estimation process. Simulation results with a large range of turbulence conditions show a noticeable improvement in the aberration estimation performance equivalent to over 3./D of modulation when using the optically preconditioned deep PWFS (DPWFS). Experimental results validate the advantages of using the designed optical layer, where the DPWFS can pair the performance of a traditional PWFS with 2./D of modulation. Designing and adding an optical preconditioner to the PWFS is just the tip of the iceberg, since the proposed deep optics methodology can be used for the design of a completely new generation of wavefront sensors that can better fit the demands of sophisticated adaptive optics applications such as ground-to-space and underwater optical communications and imaging through scattering media. (c) 2024 Chinese Laser Press