Recurrence quantification analysis as a post-processing technique in adaptive optics high-contrast imaging

TL;DR

This paper investigates the use of Recurrence Quantification Analysis (RQA) as a post-processing method to improve detection of faint astronomical objects in high-contrast imaging by discriminating signals from speckle noise.

Contribution

It introduces RQA as a novel statistical technique for high-contrast imaging data analysis, demonstrating its effectiveness on real telescope data.

Findings

RQA improves detection contrast at small angular separations.

Effective with very short data sequences (2 seconds).

Promising results compared to existing techniques.

Abstract

In this work we explore the possibility of using Recurrence Quantification Analysis (RQA) in astronomical high-contrast imaging to statistically discriminate the signal of faint objects from speckle noise. To this end, we tested RQA on a sequence of high frame rate (1 kHz) images acquired with the SHARK-VIS forerunner at the Large Binocular Telescope. Our tests show promising results in terms of detection contrasts at angular separations as small as $50$ mas, especially when RQA is applied to a very short sequence of data ($2$ s). These results are discussed in light of possible science applications and with respect to other techniques like, for example, Angular Differential Imaging and Speckle-Free Imaging.