ALF: an asymmetric Lyot wavefront sensor for the ELT/METIS vortex coronagraph

TL;DR

This paper introduces ALF, a novel wavefront sensing method using an asymmetric Lyot stop and machine learning to improve high-contrast imaging performance for the ELT/METIS vortex coronagraph, addressing atmospheric and instrumental aberrations.

Contribution

It proposes a new wavefront sensing approach combining asymmetric Lyot stops and machine learning, with simulation and laboratory validation for ELT/METIS.

Findings

Successful simulation results demonstrating wavefront reconstruction.

First laboratory demonstration confirming feasibility.

Enhanced correction of aberrations improves imaging contrast.

Abstract

Non-common path quasi-static and differential aberrations are one of the big hurdles of direct imaging for current and future high-contrast imaging instruments. They increase speckle and photon noise thus reducing the achievable contrast and lead to a significant hit in HCI performance. The Mid-infrared ELT Imager and Spectrograph (METIS) will provide high-contrast imaging, including vortex coronagraphy in L, M and N bands, with the ultimate goal of directly imaging temperate rocky planets around the nearest stars. Ground-based mid-infrared observations are however also impacted by water vapor inhomogeneities in the atmosphere, which generate additional chromatic turbulence not corrected by the near-infrared adaptive optics. This additional source of wavefront error (WFE) significantly impacts HCI performance, and even dominates the WFE budget in N band. Instantaneous focal plane wavefront sensing is thus required to mitigate its impact. In this context, we propose to implement a novel wavefront sensing approach for the vortex coronagraph using an asymmetric Lyot stop and machine learning. The asymmetric pupil stop allows for the problem to become solvable, lifting the ambiguity on the sign of even Zernike modes. Choosing the Lyot plane instead of the entrance pupil for this mask is also not arbitrary: it preserves the rejection efficiency of the coronagraph and minimizes the impact of the asymmetry on the throughput. Last but not least, machine learning allows us to solve this inversion problem which is non-linear and lacks an analytical solution. In this contribution, we present our concept, our simulation framework, our results and a first laboratory demonstration of the technique.