Karhunen-Lo\`eve Data Imputation in High Contrast Imaging

TL;DR

This paper introduces DIKL, a novel data imputation method based on the Karhunen-Loève transform, which improves high contrast imaging by reducing computational costs and integrating seamlessly into existing pipelines.

Contribution

The paper proposes DIKL, a modification of KLIP that enhances speckle removal in high contrast imaging with lower computational costs and better integration capabilities.

Findings

DIKL achieves high-quality speckle removal.

Computational cost is reduced by approximately three orders of magnitude.

DIKL is easily integrated into existing imaging pipelines.

Abstract

Detection and characterization of extended structures is a crucial goal in high contrast imaging. However, these structures face challenges in data reduction, leading to over-subtraction from speckles and self-subtraction with most existing methods. Iterative post-processing methods offer promising results, but their integration into existing pipelines is hindered by selective algorithms, high computational cost, and algorithmic regularization. To address this for reference differential imaging (RDI), here we propose the data imputation concept to Karhunen-Lo\`eve transform (DIKL) by modifying two steps in the standard Karhunen-Lo\`eve image projection (KLIP) method. Specifically, we partition an image to two matrices: an anchor matrix which focuses only on the speckles to obtain the DIKL coefficients, and a boat matrix which focuses on the regions of astrophysical interest for speckle removal using DIKL components. As an analytical approach, DIKL achieves high-quality results with significantly reduced computational cost (~3 orders of magnitude less than iterative methods). Being a derivative method of KLIP, DIKL is seamlessly integrable into high contrast imaging pipelines for RDI observations.