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Deep learning-based focal plane wavefront sensing for classical and coronagraphic imaging

High-contrast imaging instruments are today primarily limited by non-common path aberrations appearing between the wavefront sensor of the adaptive optics system and the science camera. Early attempts at using artificial neural networks for focal-plane wavefront sensing showed some successful results but today's higher computational power and deep architectures promise increased performance, flexibility and robustness that have yet to be exploited. We implement two convolutional neural networks to estimate wavefront errors from simulated point-spread functions. We notably train mixture density models and show that they can assess the ambiguity on the phase sign by predicting each Zernike coefficient as a probability distribution. Our method is also applied with the Vector Vortex coronagraph (VVC), comparing the phase retrieval performance with classical imaging. Finally, preliminary results indicate that the VVC combined with polarized light can lift the sign ambiguity.