Mitigating the Non-Linearities in a Pyramid Wavefront Sensor

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Abstract
For natural guide start adaptive optics (AO) systems, pyramid wavefront sensors (PWFSs) can provide significant increase in sensitivity over the traditional Shack-Hartmann, but at the cost of a reduced linear range. When using a linear reconstructor, non-linearities result in wavefront estimation errors, which can have a significant impact on the image quality delivered by the AO system. Here we simulate a wavefront passing through a PWFS under varying observing conditions to explore the possibility of using a non-linear machine learning model to estimate wavefront errors better than a linear reconstruction. We find significant improvement even with light-weight models, underscoring the need for further investigation of this approach.

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