Deep learning for exoplanet detection and characterization by direct imaging at high contrast
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915511618502656 |
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| author | Bodrito, Théo Flasseur, Olivier Mairal, Julien Ponce, Jean Langlois, Maud Lagrange, Anne-Marie |
| author_facet | Bodrito, Théo Flasseur, Olivier Mairal, Julien Ponce, Jean Langlois, Maud Lagrange, Anne-Marie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20310 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning for exoplanet detection and characterization by direct imaging at high contrast Bodrito, Théo Flasseur, Olivier Mairal, Julien Ponce, Jean Langlois, Maud Lagrange, Anne-Marie Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Machine Learning 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. |
| title | Deep learning for exoplanet detection and characterization by direct imaging at high contrast |
| topic | Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2509.20310 |