Direct Exoplanet Detection Using L1 Norm Low-Rank Approximation

TL;DR

This paper introduces a novel L1-norm based low-rank approximation method for direct exoplanet imaging, addressing challenges like star brightness, small angular separation, and speckle noise, and compares it with traditional L2-based methods.

Contribution

It proposes an L1-norm low-rank approximation algorithm for exoplanet detection and evaluates its effectiveness against standard L2-based methods using empirical data.

Findings

L1-LRA outperforms L2-based SVD in detecting exoplanets.

L1-LRA shows better noise robustness in speckle-affected images.

The method improves detection accuracy near the star and in distant regions.

Abstract

We propose to use low-rank matrix approximation using the component-wise L1-norm for direct imaging of exoplanets. Exoplanet detection by direct imaging is a challenging task for three main reasons: (1) the host star is several orders of magnitude brighter than exoplanets, (2) the angular distance between exoplanets and star is usually very small, and (3) the images are affected by the noises called speckles that are very similar to the exoplanet signal both in shape and intensity. We first empirically examine the statistical noise assumptions of the L1 and L2 models, and then we evaluate the performance of the proposed L1 low-rank approximation (L1-LRA) algorithm based on visual comparisons and receiver operating characteristic (ROC) curves. We compare the results of the L1-LRA with the widely used truncated singular value decomposition (SVD) based on the L2 norm in two different annuli, one close to the star and one far away.