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Speckle Space–Time Covariance in High-contrast Imaging


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.

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