Exploring reinforcement learning to enhance focal-plane wavefront control for vortex coronagraphs
Abstract
High Contrast Imaging (HCI) on ground-based telescopes suffers from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are adept at correcting these aberrations, but fall short in the correction of non-common path aberrations (NCPAs). NCPAs arise because the wavefront sensor (WFS) measures and corrects a wavefront that is different from that affecting the science images, thus requiring additional intervention. This work makes use of focal-plane wavefront sensing and reinforcement learning (RL) to address the wavefront aberrations caused by NCPAs. The PO4NCPA algorithm utilizes sequential phase diversity to address phase ambiguities and is tested on a simulation designed for the Mid-infrared ELT Imager and Spectrograph (METIS) instrument. In this paper, we present the performance of PO4NCPA with scalar and vector vortex coronagraphs to demonstrate its flexibility.
Versions
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BibTeX
@misc{taskin2026exploring,
title = {{Exploring reinforcement learning to enhance focal-plane wavefront control for vortex coronagraphs}},
author = {Iremsu Taskin and Jalo Nousiainen and Gilles Orban de Xivry and Olivier Absil and Markus Kasper},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01199},
eprint = {2609.01199},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01199},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Exploring reinforcement learning to enhance focal-plane wavefront control for vortex coronagraphs
AU - Iremsu Taskin
AU - Jalo Nousiainen
AU - Gilles Orban de Xivry
AU - Olivier Absil
AU - Markus Kasper
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01199
UR - https://arxiv.org/abs/2609.01199
AB - High Contrast Imaging (HCI) on ground-based telescopes suffers from phase aberrations on the observed wavefront caused by atmospheric turbulence. Adaptive Optics (AO) systems are adept at correcting these aberrations, but fall short in the correction of non-common path aberrations (NCPAs). NCPAs arise because the wavefront sensor (WFS) measures and corrects a wavefront that is different from that affecting the science images, thus requiring additional intervention. This work makes use of focal-plane wavefront sensing and reinforcement learning (RL) to address the wavefront aberrations caused by NCPAs. The PO4NCPA algorithm utilizes sequential phase diversity to address phase ambiguities and is tested on a simulation designed for the Mid-infrared ELT Imager and Spectrograph (METIS) instrument. In this paper, we present the performance of PO4NCPA with scalar and vector vortex coronagraphs to demonstrate its flexibility.
ER -