Communication Dans Un Congrès Année : 2025

Deep learning for exoplanet detection and characterization by direct imaging at high contrast

Résumé

Exoplanet imaging is a major challenge in astrophysics due to the need for high angular resolution and high contrast. We present a multi-scale statistical model for the nuisance component corrupting multivariate image series at high contrast. Integrated into a learnable architecture, it leverages the physics of the problem and enables the fusion of multiple observations of the same star in a way that is optimal in terms of detection signal-to-noise ratio. Applied to data from the VLT/SPHERE instrument, the method significantly improves the detection sensitivity and the accuracy of astrometric and photometric estimation.

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Dates et versions

hal-05281730 , version 1 (24-09-2025)

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  • HAL Id : hal-05281730 , version 1

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Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, et al.. Deep learning for exoplanet detection and characterization by direct imaging at high contrast. SF2A 2025 - Journées de la Société Française d’Astronomie & d’Astrophysique, Jul 2025, Toulouse, France. pp.1-5. ⟨hal-05281730⟩
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