Machine learning assisted wavefront sensor.

Abstract

Adaptive optics aims to restore diffraction limited resolution in an imaging system in the presence of optical aberrations. To this end, the wavefront error needs to be measured prior to its compensation. Traditional wavefront sensors typically measure the local gradient of the wavefront emitted from a guide star (or another known point source) using a microlens array in front of a camera. They are typically dedicated devices that need to be integrated into an imaging system. Here we have tested the concept of estimating the wavefront error directly from an image of a guide star by training a machine learning model. We produce a two-photon laser spot in a water fluorescein media, and introduce random, but known aberrations using a deformable mirror to form a large training set. After validation, we integrated the machine learning wavefront sensor in an AO feedback loop with the deformable mirror. We demonstrate proof of principle compensation of sample-introduced optical aberrations with this system.