Wavefront error tolerancing for direct imaging of exo-Earths with a large segmented telescope in space
Iva Laginja, Lucie Leboulleux, Laurent Pueyo, R\'emi Soummer,, Jean-Fran\c{c}ois Sauvage, Laurent Mugnier, Laura E. Coyle, J. Scott Knight,, Kathryn St. Laurent, Emiel H. Por, James Noss

TL;DR
This paper extends a model for analyzing wavefront errors in segmented space telescopes, enabling precise tolerancing for high-contrast imaging of exo-Earths, crucial for future space observatories.
Contribution
It introduces a semi-analytical extension of the PASTIS model, allowing for efficient wavefront error tolerancing in segmented space telescopes.
Findings
Wavefront error tolerances between 56 pm and 290 pm per segment.
The model can be applied to various segment-level aberrations.
The approach aids in establishing stability requirements for space telescopes.
Abstract
Direct imaging of exo-Earths and search for life is one of the most exciting and challenging objectives for future space observatories. Segmented apertures in space will be required to reach the needed large diameters beyond the capabilities of current or planned launch vehicles. These apertures present additional challenges for high-contrast coronagraphy, not only in terms of static phasing but also in terms of their stability. The Pair-based Analytical model for Segmented Telescope Imaging from Space (PASTIS) was developed to model the effects of segment-level optical aberrations on the final image contrast. In this paper, we extend the original PASTIS propagation model from a purely analytical to a semi-analytical method, in which we substitute the use of analytical images with numerically simulated images. The inversion of this model yields a set of orthonormal modes that can be…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
