Lightweight starshade position sensing with convolutional neural networks and simulation-based inference

TL;DR

This paper introduces a lightweight convolutional neural network approach combined with simulation-based inference for precise starshade position sensing, enabling efficient real-time alignment in space telescopes with minimal computational resources.

Contribution

The authors develop and validate a CNN-based method that accurately estimates starshade position using simulated training data, suitable for resource-constrained spacecraft.

Findings

Achieves centimeter-level accuracy across the pupil plane.

Requires only 1.6 MB of data and 5.3 MFLOPs per image.

Successfully integrated into a real testbed for closed-loop control.

Abstract

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.