Nicolás Hernandez

Pontificia Universidad Católica de Valparaíso, Chile 

The Pyramid Wavefront Sensor (PWFS) is one of the preferred choices for measuring wavefront aberrations for adaptive optics in highly sensitive astronomical applications. Despite its high sensitivity, the PWFS offers a limited linear range of operation that can only be extended through the modulation of the pyramid, which involves additional mechanical and movable parts, complicating the optical setup and its calibration and operation. Nevertheless, modulation decreases the sensitivity in exchange of the extended linearity range. Inspired by artificial intelligence techniques, we designed a passive diffractive layer that acts as an optical preconditioner to the PWFS, with the aim of extending the linearity range without the need for modulation. The design of the novel Deep Optics PWFS (DWPFS) is obtained through an End-2-End optimization scheme that involves, firstly, the physical modeling of the optical propagation path of the modified PWFS up to the measured intensity patterns at the detector level, and secondly, the mathematical estimation using a linear model. Then, the estimation error is used to feed the backpropagation through the models to update the diffractive layer. Considering as the initial point the PWFS without modulation, the training process is performed using a variety of wavefront maps from realistic turbulence models at different strengths, most likely situated in the zone where we want to force the response of the sensor to become more linear. Simulation and experimental results suggest that the DPWFS excel for estimating turbulence without requiring modulation, being competitive to the PWFS with modulation.