How to train your Jacobian: least-squares system identiﬁcation for space-based coronagraphy

Scott D. Will; Meiji Nguyen; Raphaël Pourcelot; Hari B. Subedi; Tyler D. Groff; Rémi Soummer

doi:10.1117/12.3020344

23 August 2024 How to train your Jacobian: least-squares system identiﬁcation for space-based coronagraphy

Scott D. Will, Meiji Nguyen, Raphaël Pourcelot, Hari B. Subedi, Tyler D. Groff, Rémi Soummer

Author Affiliations +

Proceedings Volume 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave; 1309226 (2024) https://doi.org/10.1117/12.3020344
Event: SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Abstract

Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. Model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the assumed model causes reduced control loop performance. Here, we report on a collection of techniques, including prediction-error minimization, expectation-maximization, and maximum-likelihood estimation, for empirically tuning the wavefront control Jacobian matrix in a statistically rigorous fashion during closed-loop wavefront control operations. This mitigates model mismatch and recovers near-optimal control loop performance.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Scott D. Will, Meiji Nguyen, Raphaël Pourcelot, Hari B. Subedi, Tyler D. Groff, and Rémi Soummer "How to train your Jacobian: least-squares system identiﬁcation for space-based coronagraphy", Proc. SPIE 13092, Space Telescopes and Instrumentation 2024: Optical, Infrared, and Millimeter Wave, 1309226 (23 August 2024); https://doi.org/10.1117/12.3020344

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