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

Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois +1 more
9/24/2025
astro-ph.IMastro-ph.EPcs.LG

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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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Cite this paper

@article{bodrito2025deep,
  title  = {Deep learning for exoplanet detection and characterization by direct imaging at high contrast},
  author = {Théo Bodrito and Olivier Flasseur and Julien Mairal and Jean Ponce and Maud Langlois and Anne-Marie Lagrange},
  year   = {2025},
  eprint = {2509.20310},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.20310v1}
}

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