Sequential Covariance Calculation for Exoplanet Image Processing

TL;DR

This paper introduces efficient sequential covariance calculation methods to enhance the computational performance of post-processing algorithms used in exoplanet image analysis, where noise decorrelation is critical.

Contribution

It presents a novel approach to compute covariances sequentially, reducing computational costs in algorithms for exoplanet imaging data processing.

Findings

Sequential covariance calculation improves efficiency

Algorithms effectively decorrelate noise in exoplanet images

Method enhances processing speed for large datasets

Abstract

Direct imaging of exoplanets involves the extraction of very faint signals from highly noisy data sets, with noise that often exhibits significant spatial, spectral and temporal correlations. As a results, a large number of post-processing algorithms have been developed in order to optimally decorrelate the signal from the noise. In this paper, we explore four such closely related algorithms, all of which depend heavily on the calculation of covariances between large data sets of imaging data. We discuss the similarities and differences between these methods, and demonstrate how the use sequential calculation techniques can significantly improve their computational efficiencies.