Dr. Olivier Flasseur Profile

Dr. Olivier Flasseur

at CRAL / CNRS

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Publications (8)

Proceedings Article | 30 August 2022 Poster + Paper

Optimal multi-epoch combination of direct imaging observations for improved exoplanet detection

Jules Dallant, Maud Langlois, Éric Thiébaut, Olivier Flasseur

Proceedings Volume 12185, 1218537 (2022) https://doi.org/10.1117/12.2630053

KEYWORDS: Signal to noise ratio, Exoplanets, Detection and tracking algorithms, Planets, Exoplanetary science, Stars, Sensors, Error analysis, Data modeling, Astrophysics

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Proceedings Article | 30 August 2022 Poster + Paper

Exoplanet detection in angular differential imaging: combining a statistics-based learning with a deep-based learning for improved detections

Olivier Flasseur, Théo Bodrito, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange

Proceedings Volume 12185, 121853S (2022) https://doi.org/10.1117/12.2629849

KEYWORDS: Data modeling, Statistical analysis, Exoplanetary science, Detection and tracking algorithms

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Direct imaging is an active research topic in astronomy for the detection and the characterization of young substellar objects. The very high contrast between the host star and its companions makes detection particularly challenging. In addition to the use of an extreme adaptive optics system and a coronagraph to strongly attenuate the starlight contamination, dedicated post-processing methods combining several images recorded with the pupil tracking mode of the telescope are needed. In previous works, we have presented the PACO algorithm capturing the spatial correlations of the data with a multi-variate Gaussian model whose parameters are estimated in a data-driven fashion at the scale of a patch of a few tens of pixels. PACO is parameter free and delivers reliable detection confidences with an improved sensitivity compared to the standard methods of the field (e.g., cADI, PCA, TLOCI ). However, there is a room for improvement in the detection sensitivity due to the approximate fidelity of the PACO statistical model with respect to the observations. We propose to combine the statistics-based model of PACO with a deep learning approach in a three-step algorithm. First, the data are centered and whitened locally using the PACO framework to improve the stationarity and the contrast in a preprocessing step. Second, a convolutional neural network is trained in a supervised fashion to detect the signature of synthetic sources in the preprocessed science data. The network is trained from scratch with a custom data augmentation strategy allowing to generate a large training set from a single spatio-temporal dataset. Finally, the trained network is applied to the preprocessed observations and delivers a detection map. We apply our method on eleven datasets from the VLT/SPHERE-IRDIS instrument and compare our method with PACO and other baselines of the field (cADI, PCA). Our results show that the proposed method performs on-par with or better than these algorithms, with a contrast improvement up to half a magnitude with respect to PACO.

Proceedings Article | 30 August 2022 Poster + Paper

Multispectral image reconstruction of faint circumstellar environments from high contrast angular spectral differential imaging (ASDI) data

Olivier Flasseur, Samuel Thé, Loïc Denis, Éric Thiébaut, Maud Langlois

Proceedings Volume 12185, 121853U (2022) https://doi.org/10.1117/12.2630362

KEYWORDS: Reconstruction algorithms, Statistical analysis, Data modeling, Statistical modeling

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Proceedings Article | 13 December 2020 Poster + Paper

Exoplanet imaging data challenge: benchmarking the various image processing methods for exoplanet detection

F. Cantalloube, C. Gomez-Gonzalez, O. Absil, C. Cantero, R. Bacher, M. Bonse, M. Bottom, C.-H. Dahlqvist, C. Desgrange, O. Flasseur, T. Fuhrmann, Th. Henning, R. Jensen-Clem, M. Kenworthy, D. Mawet, D. Mesa, T. Meshkat, D. Mouillet, A. Müller, E. Nasedkin, B. Pairet, S. Piérard, J.-B. Ruffio, M. Samland, J. Stone, M. Van Droogenbroeck

Proceedings Volume 11448, 114485A (2020) https://doi.org/10.1117/12.2574803

KEYWORDS: Exoplanets, Algorithm development, Planets, Stars, Adaptive optics, Coronagraphy, Interference (communication), Detection and tracking algorithms

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Proceedings Article | 30 March 2020 Presentation

Accounting for the nonstationary correlated noise in digital holography (Conference Presentation)

Olivier Flasseur, Loïc Denis, Éric Thiébaut, Thomas Olivier, Corinne Fournier

Proceedings Volume 11351, 1135113 (2020) https://doi.org/10.1117/12.2559941

KEYWORDS: Digital holography, Signal detection, Diffraction, Reflection, Optical components, Statistical analysis, Algorithm development, Detection and tracking algorithms, Holography, Microscopes

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Proceedings Article | 10 July 2018 Paper

Exoplanet detection in angular and spectral differential imaging: local learning of background correlations for improved detections

Olivier Flasseur, Loïc Denis, Éric Thiébaut, Maud Langlois

Proceedings Volume 10703, 107032R (2018) https://doi.org/10.1117/12.2313601

KEYWORDS: Exoplanets, Signal to noise ratio, Statistical analysis, Stars, Detection and tracking algorithms, Exoplanetary science, Point spread functions, Binary data, Spectrographs, Imaging systems

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