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

• URL: http://arxiv.org/abs/2509.20310v1
• Date: Wed, 24 Sep 2025 16:43:28 GMT
• Title: Deep learning for exoplanet detection and characterization by direct imaging at high contrast
• Authors: Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange,
• Abstract summary: We present a multi-scale statistical model for the nuisance component corrupting multivariate image series at high contrast.<n> Applied to data from the VLT/SPHERE instrument, the method significantly improves the detection sensitivity and the accuracy of astrometric and photometric estimation.
• Score: 26.359902459074856
• License: http://creativecommons.org/licenses/by/4.0/
• 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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