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

Andrew Chen, Anthony Harness, Peter Melchior

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Starshades are a leading technology to enable the direct detection and spectroscopic characterization of Earth-like exoplanets. To maintain high contrast during observations, the starshade and telescope must keep within 1 m of relative alignment over large separations (>20,000 km). This formation flying is made possible with precise spacecraft position information obtained through accurate sensing of the occulted star's diffraction peak (referred to as the spot of Arago) incident on the telescope aperture. 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. On simulated images, the method achieves an accuracy of a few centimeters across the entire telescope aperture. 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.

Original languageEnglish (US)
Article number025002
JournalJournal of Astronomical Telescopes, Instruments, and Systems
Volume9
Issue number2
DOIs
StatePublished - Apr 1 2023

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Control and Systems Engineering
  • Instrumentation
  • Astronomy and Astrophysics
  • Mechanical Engineering
  • Space and Planetary Science

Keywords

  • convolutional neural network
  • formation flying
  • simulation-based inference
  • starshade

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