{"ID":2866701,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.20310","arxiv_id":"2509.20310","title":"Deep learning for exoplanet detection and characterization by direct imaging at high contrast","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.","short_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 pro...","url_abs":"https://arxiv.org/abs/2509.20310","url_pdf":"https://arxiv.org/pdf/2509.20310v1","authors":"[\"Théo Bodrito\",\"Olivier Flasseur\",\"Julien Mairal\",\"Jean Ponce\",\"Maud Langlois\",\"Anne-Marie Lagrange\"]","published":"2025-09-24T16:43:28Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"astro-ph.EP\",\"cs.LG\"]","methods":"[]","has_code":false}