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Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

Gomez Gonzalez, C. A. and Absil, O. and Absil, P.-A. and Van Droogenbroeck, M. and Mawet, D. and Surdej, J. (2016) Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm. Astronomy and Astrophysics, 589 . Art. No. A54. ISSN 0004-6361. doi:10.1051/0004-6361/201527387.

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Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims. Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods. We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results. Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Gomez Gonzalez, C. A.0000-0003-2050-1710
Absil, O.0000-0002-4006-6237
Absil, P.-A.0000-0003-2946-4178
Mawet, D.0000-0002-8895-4735
Surdej, J.0000-0002-7005-1976
Additional Information:© 2016 ESO. Article published by EDP Sciences. Received 17 September 2015; Accepted 20 January 2016; Published online 13 April 2016. We would like to thank the anonymous referee for the very useful comments that helped us improve the quality of this paper. The research leading to these results has received funding from the European Research Council Under the European Union’s Seventh Framework Program (ERC Grant Agreement No. 337569) and from the French Community of Belgium through an ARC grant for Concerted Research Action.
Funding AgencyGrant Number
European Research Council (ERC)337569
Actions de Recherche Concertees (Communauté Française de Belgique)UNSPECIFIED
Subject Keywords:methods: data analysis – techniques: high angular resolution – techniques: image processing – planetary systems – planets and satellites: detection
Record Number:CaltechAUTHORS:20160405-195255205
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Official Citation:Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences - The LLSG algorithm C. A. Gomez Gonzalez, O. Absil, P.-A. Absil, M. Van Droogenbroeck, D. Mawet and J. Surdej A&A, 589 (2016) A54 DOI:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65949
Deposited By: Joy Painter
Deposited On:06 Apr 2016 18:09
Last Modified:10 Nov 2021 23:51

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