Millisecond Exoplanet Imaging, I: Method and Simulation Results

TL;DR

This paper introduces a novel millisecond regression method for direct exoplanet imaging that effectively estimates and corrects non-common path aberrations, significantly improving contrast and image quality in simulated ground-based observations.

Contribution

It develops and tests a bias-corrected regression model for real-time NCPA estimation, enhancing exoplanet imaging contrast and artifact reduction compared to previous methods.

Findings

Achieved NCPA estimation accuracy of ~0.005 radians

Demonstrated contrast of ~10^-5 in simulations

Produced artifact-free exoplanet images comparable to ideal PSF subtraction

Abstract

One of the top remaining science challenges in astronomical optics is the direct imaging and characterization of extrasolar planets and planetary systems. Directly imaging exoplanets from ground-based observatories requires combining high-order adaptive optics with a stellar coronagraph observing at wavelengths ranging from the visible to the mid-IR. A limiting factor in achieving the required contrast (planet-to-star intensity ratio) is quasi-static speckles, caused largely by non-common path aberrations (NCPA) in the coronagraph. Starting with a realistic simulator of a telescope with an AO system and a coronagraph, this article provides simulations of several closely related millisecond regression models requiring inputs of the measured wavefronts and science camera images. The simplest regression model, called the naive estimator, does not treat the noise and other sources of information loss in the WFS. The naive estimator provided a useful estimate of the NCPA of $\sim$ 0.5 radian RMS, with an accuracy of $\sim$ 0.06 radian RMS in one minute of simulated sky time on a magnitude 8 star. The bias-corrected estimator generalizes the regression model to account for the noise and information loss in the WFS. A simulation of the bias-corrected estimator with four minutes of sky time included an NCPA of $\sim 0.05 \,$ radian RMS and an extended exoplanet scene. The joint regression of the bias-corrected estimator simultaneously achieved an NCPA estimate with an accuracy of $\sim 5\times10^{-3} \,$radian and contrast of $\sim 10^{-5}$ on the exoplanet scene. In addition, the estimate of the exoplanet image was completely free of the subtraction artifacts that always plague differential imaging. The estimate of the exoplanet image obtained by the joint regression was nearly identical to the image obtained by subtraction of a perfectly known point-spread function.