Piston alignment of segmented optical mirrors via convolutional neural networks

Most of the methods used today for the alignment of segmented mirrors are based on Shack-Hartman wavefront sensors. Other proposed methods are based on curvature sensors. These can be used to cross-check the measurements given by the primary method. We investigate a different approach which employs convolutional neural networks. This technique allows the piston step values between segments to be measured with high accuracy, as well as a large capture range at visible wavelengths. The technique does not require special hardware, and is fast to be used at any time during the observation.