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

arXiv: https://arxiv.org/abs/2509.20310 · HTML: https://mlsanity.com/paper/2509.20310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2025.

## Abstract (arXiv)

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.