Fast, Efficient Phasing of Deployable Space Telescopes using Machine Learning

Daniel Martin, Cyril Bourgenot, Andrew Reeves and Hubert P. H. Shum
Optics Express, 2026

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Fast, Efficient Phasing of Deployable Space Telescopes using Machine Learning

Abstract

A fast, two-step piston sensing technique has been developed to enable diffraction limited imaging. Achieving such performance requires a Strehl ratio exceeding 0.8, which corresponds to a wavefront RMS error below 32 nm at λ = 450 nm. A machine learning model has been implemented for a four-petal telescope to retrieve piston errors directly from PSF images and enable mirror correction. When tested on synthetic misalignments drawn from a uniform distribution within ±300 nm ( or ±2λ/3), the model improved the mean Strehl ratio from a degraded state to 0.95 after one iteration, and to 0.99 after a second iteration. An SNR greater than 40 was found sufficient to achieve phasing corresponding to a Strehl ratio of at least 0.97.


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Plain Text

Daniel Martin, Cyril Bourgenot, Andrew Reeves and Hubert P. H. Shum, "Fast, Efficient Phasing of Deployable Space Telescopes using Machine Learning," Optics Express, Optica, 2026.

BibTeX

@article{martin26fast,
 author={Martin, Daniel and Bourgenot, Cyril and Reeves, Andrew and Shum, Hubert P. H.},
 journal={Optics Express},
 title={Fast, Efficient Phasing of Deployable Space Telescopes using Machine Learning},
 year={2026},
 publisher={Optica},
}

RIS

TY  - JOUR
AU  - Martin, Daniel
AU  - Bourgenot, Cyril
AU  - Reeves, Andrew
AU  - Shum, Hubert P. H.
T2  - Optics Express
TI  - Fast, Efficient Phasing of Deployable Space Telescopes using Machine Learning
PY  - 2026
PB  - Optica
ER  - 


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