  Starshades are a leading technology to enable the direct detection and
spectroscopic characterization of Earth-like exoplanets. To keep the starshade
and telescope aligned over large separations, reliable sensing of the peak of
the diffracted light of the occluded star is required. Current techniques rely
on image matching or model fitting, both of which put substantial computational
burdens on resource-limited spacecraft computers. We present a lightweight
image processing method based on a convolutional neural network paired with a
simulation-based inference technique to estimate the position of the spot of
Arago and its uncertainty. The method achieves an accuracy of a few centimeters
across the entire pupil plane, while only requiring 1.6 MB in stored data
structures and 5.3 MFLOPs (million floating point operations) per image at test
time. By deploying our method at the Princeton Starshade Testbed, we
demonstrate that the neural network can be trained on simulated images and used
on real images, and that it can successfully be integrated in the control
system for closed-loop formation flying.
