Speckle Space-Time Covariance in High-Contrast Imaging

TL;DR

This paper presents a novel PSF subtraction method leveraging the spatio-temporal covariance of speckle noise in high-contrast imaging, utilizing a Karhunen-Loève transform on image sequences to enhance contrast, especially with photon-counting detectors.

Contribution

The paper introduces a new space-time covariance-based PSF subtraction framework using Karhunen-Loève transform on image sequences, improving contrast in high-contrast imaging.

Findings

Improves contrast by 10-20% in simulated data.

Potential to outperform spatial-only KLIP under certain parameters.

Applicable with photon-counting detectors like MKIDs.

Abstract

We introduce a new framework for point-spread function (PSF) subtraction based on the spatio-temporal variation of speckle noise in high-contrast imaging data where the sampling timescale is faster than the speckle evolution timescale. One way that space-time covariance arises in the pupil is as atmospheric layers translate across the telescope aperture and create small, time-varying perturbations in the phase of the incoming wavefront. The propagation of this field to the focal plane preserves some of that space-time covariance. To utilize this covariance, our new approach uses a Karhunen-Lo\'eve transform on an image sequence, as opposed to a set of single reference images as in previous applications of Karhunen-Lo\'eve Image Processing (KLIP) for high-contrast imaging. With the recent development of photon-counting detectors, such as microwave kinetic inductance detectors (MKIDs), this technique now has the potential to improve contrast when used as a post-processing step. Preliminary testing on simulated data shows this technique can improve contrast by at least 10-20% from the original image, with significant potential for further improvement. For certain choices of parameters, this algorithm may provide larger contrast gains than spatial-only KLIP.