High contrast imaging wavefront sensor referencing from coronagraphic images

TL;DR

This paper introduces DrWHO, a novel image-based method for real-time compensation of non-common path aberrations in high contrast imaging, improving exoplanet detection by enhancing wavefront correction without relying on detailed models.

Contribution

The paper presents DrWHO, a new quasi-real-time, image-based wavefront correction algorithm that does not depend on wavefront sensor calibration or models, applicable to non-linear sensing situations.

Findings

First on-sky results with Subaru SCExAO demonstrate effectiveness.

DrWHO improves contrast by continuously updating wavefront references.

Method is applicable to static, quasi-static, and dynamic aberrations.

Abstract

A key challenge of high contrast imaging (HCI) is to differentiate a speckle from an exoplanet signal. The sources of speckles are a combination of atmospheric residuals and aberrations in the non-common path. Those non-common path aberrations (NCPA) are particularly challenging to compensate for as they are not directly measured, and because they include static, quasi-static and dynamic components. The proposed method directly addresses the challenge of compensating the NCPA. The algorithm DrWHO - Direct Reinforcement Wavefront Heuristic Optimisation - is a quasi-real-time compensation of static and dynamic NCPA for boosting image contrast. It is an image-based lucky imaging approach, aimed at finding and continuously updating the ideal reference of the wavefront sensor (WFS) that includes the NCPA, and updating this new reference to the WFS. Doing so changes the point of convergence of the AO loop. We show here the first results of a post-coronagraphic application of DrWHO. DrWHO does not rely on any model nor requires accurate wavefront sensor calibration, and is applicable to non-linear wavefront sensing situations. We present on-sky performances using a pyramid WFS sensor with the Subaru coronagraph extreme AO (SCExAO) instrument.