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arXiv · 2509.20310

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

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

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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Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange. 2025-09-24. Deep learning for exoplanet detection and characterization by direct imaging at high contrast. https://arxiv.org/abs/2509.20310

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