Speckle Space–Time Covariance in High-contrast Imaging

Abstract

We introduce a new framework for point-spread function subtraction based on the spatiotemporal 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–Loève transform on an image sequence, as opposed to a set of single reference images as in previous applications of Karhunen–Loève Image Processing (KLIP) for high-contrast imaging. With the recent development of photon-counting detectors, such as microwave kinetic inductance detectors, 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.