Overcoming the low signal-to-noise problem for hybrid mode-selective photonic lantern-based wavefront correction using machine learning
Abstract
Hybrid mode-selective photonic lanterns transform an input complex point-spread function into several single-mode outputs, where a selected core feeds the fundamental mode to a photonic science instrument, while the remaining cores are used for wavefront sensing in a closed-loop adaptive optics system. A neural network maps the intensities of the wavefront sensing cores to an estimated wavefront correction, which is applied to an upstream deformable mirror. However, there exists a trade between maximizing the amount of light reserved for the photonic instrument and the reduced signal-to-noise ratios for the wavefront sensing cores. We explore wavefront correction for the Seidr instrument, a part of the Asgard Suite for the Very Large Telescope Interferometer. We evaluate different neural network architectures, comparing wavefront estimation performance for different wavefront error types, as a first step toward addressing the signal-to-noise trade-off.
Cite
@article{arxiv.2607.03285,
title = {Overcoming the low signal-to-noise problem for hybrid mode-selective photonic lantern-based wavefront correction using machine learning},
author = {Nathan K Long and Barnaby Norris and Daniel S Dahl and Christopher H Betters and Julia J Bryant and Nick Cvetojevic and Sergio Leon-Saval and Frantz Martinache and Marc-Antoine Martinod and Akira Rodziewicz-Ryan and Adam K Taras and Jin Wei and Peter G Tuthill},
journal= {arXiv preprint arXiv:2607.03285},
year = {2026}
}