Deep Optics Preconditioner for Modulation-free Pyramid Wavefront Sensing

The Pyramid Wavefront Sensor (PWFS) can provide with the sensitivity needed for demanding adaptive optics applications, such as imaging exoplanets using the future extremely large telescopes of over 30 meters of diameter. However, its exquisite sensitivity has a limited linear range of operation. Linearity, or dynamic range, can only be extended through the use of beam modulation, at the expense of sacrificing sensitivity and the addition of modulation optics in the sensor’s optical path. Inspired by artificial intelligence techniques, this work proposes to train an optical layer–comprised of a passive diffractive element placed at a conjugated Fourier plane where the tip of the pyramid lies–to boost the linear response of the pyramid sensor without the need for the cumbersome modulation. We develop an End-2-End simulation to train the single-layer optical preconditioner, while assuming a traditional least-square modal phase estimation. Simulation results with a large range of turbulence conditions show a noticeable improvement in the aberration estimation performance equivalent to over 3λ/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 2λ/D of modulation. Designing and adding an optical preconditioner to the PWFS is just the tip of the iceberg, since the same methodology herein developed can be used for the design of a new generation of wavefront sensors that can better fit the demands of sophisticated adaptive optics applications such as space and underwater optical communications, and imaging through scattering media.