Low-rank plus sparse trajectory decomposition for direct exoplanet imaging

TL;DR

This paper introduces a novel direct exoplanet imaging method that combines low-rank background modeling with structured sparse foreground detection, utilizing a trajectory dictionary and an iterative thresholding algorithm.

Contribution

It develops a low-rank plus sparse model with a trajectory dictionary and an efficient algorithm, improving exoplanet detection over existing PCA methods.

Findings

Potential to outperform Annular PCA in ROC performance

Effective modeling of exoplanet trajectories during observation

Demonstrated on the β-Pictoris dataset

Abstract

We propose a direct imaging method for the detection of exoplanets based on a combined low-rank plus structured sparse model. For this task, we develop a dictionary of possible effective circular trajectories a planet can take during the observation time, elements of which can be efficiently computed using rotation and convolution operation. We design a simple alternating iterative hard-thresholding algorithm that jointly promotes a low-rank background and a sparse exoplanet foreground, to solve the non-convex optimisation problem. The experimental comparison on the $\beta$-Pictoris exoplanet benchmark dataset shows that our method has the potential to outperform the widely used Annular PCA for specific planet light intensities in terms of the Receiver operating characteristic (ROC) curves.