Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data

Cantalloube, F.; Mouillet, D.; Mugnier, Laurent M.; Milli, J.; Absil, O.; González, Carlos Gómez; Chauvin, G.; Beuzit, J. L.; Cornia, A.

doi:10.1051/0004-6361/201425571

Cited by 118 publications

(130 citation statements)

References 32 publications

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“…ADI can be paired with several post-processing algorithms, such as the least-squares based LOCI (locally optimized combination of images, Lafrenière et al 2007), the maximum likelihood based ANDROMEDA (Mugnier et al 2009;Cantalloube et al 2015), and the family of principal component analysis (PCA) based algorithms (Amara & Quanz 2012;Soummer et al 2012). Recent algorithms such as LLSG (Gomez Gonzalez et al 2016a) aim to decompose the images into lowrank, sparse, and Gaussian-noise terms in order to separate the companion signal from the star point-spread function (PSF) and speckle field.…”

Section: Introductionmentioning

confidence: 99%

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

et al. 2017

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We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical highcontrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular differential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompassing pre-and postprocessing algorithms, potential sourceposition and flux estimation, and sensitivity curvegeneration. Among the reference point-spreadfunction subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithms capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization, which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR 8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP, we investigated the presence of additional companions around HR 8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github. com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library.

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Section: Introductionmentioning

confidence: 99%

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

et al. 2017

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“…This approach provides a significant gain in sensitivity compared to the classical median reference PSF. ANDROMEDA (ANgular Differential OptiMal Exoplanet Detection Algorithm, Mugnier et al 2009;Cantalloube et al 2015) treats the ADI sequence in a pair-wise way and ensures that the images are chosen close enough in time to guarantee the stability of the speckle noise and thereby allow its suppression. In this approach the second image from every pair is used as a reference PSF for the first.…”

Section: Introductionmentioning

confidence: 99%

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences

González

Absil

et al. 2016

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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.

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“…The maximum likelihood approach of ANDROMEDA, while a step in this direction, has not been thoroughly benchmarked against state-of-the-art approaches. Comparative contrast curves show its performance to be at the same level as full-frame ADI-PCA (Cantalloube et al 2015).…”

Section: State-of-the-art Image Processing Techniques For Hcimentioning

confidence: 93%

“…All these approaches use different types of low-rank approximation to generate a model PSF. A different approach is taken by ANDROMEDA (Mugnier et al 2009;Cantalloube et al 2015), which employs maximum likelihood estimation on residual images obtained by pairwise subtraction within the ADI sequence.…”

Section: State-of-the-art Image Processing Techniques For Hcimentioning

confidence: 99%

Supervised detection of exoplanets in high-contrast imaging sequences

González

Absil

Droogenbroeck

2018

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Context. Post-processing algorithms play a key role in pushing the detection limits of high-contrast imaging (HCI) instruments. State-of-the-art image processing approaches for HCI enable the production of science-ready images relying on unsupervised learning techniques, such as low-rank approximations, for generating a model PSF and subtracting the residual starlight and speckle noise. Aims. In order to maximize the detection rate of HCI instruments and survey campaigns, advanced algorithms with higher sensitivities to faint companions are needed, especially for the speckle-dominated innermost region of the images. Methods. We propose a reformulation of the exoplanet detection task (for ADI sequences) that builds on well-established machine learning techniques to take HCI post-processing from an unsupervised to a supervised learning context. In this new framework, we present algorithmic solutions using two different discriminative models: SODIRF (random forests) and SODINN (neural networks). We test these algorithms on real ADI datasets from VLT/NACO and VLT/SPHERE HCI instruments. We then assess their performances by injecting fake companions and using receiver operating characteristic analysis. This is done in comparison with state-of-the-art ADI algorithms, such as ADI principal component analysis (ADI-PCA).Results. This study shows the improved sensitivity vs specificity trade-off of the proposed supervised detection approach. At the diffraction limit, SODINN improves the true positive rate by a factor ranging from ∼2 to ∼10 (depending on the dataset and angular separation) with respect to ADI-PCA when working at the same false positive level. Conclusions. The proposed supervised detection framework outperforms state-of-the-art techniques in the task of discriminating planet signal from speckles. In addition, it offers the possibility of re-processing existing HCI databases to maximize their scientific return and potentially improve the demographics of directly imaged exoplanets.

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Direct exoplanet detection and characterization using the ANDROMEDA method: Performance on VLT/NaCo data

Cited by 118 publications

References 32 publications

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences

Supervised detection of exoplanets in high-contrast imaging sequences

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