Deep learning for exoplanet detection and characterization by direct imaging at high contrast
Théo Bodrito, Olivier Flasseur, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange
系外行星成像是天体物理学的主要挑战,因为需要高角分辨率和高对比度。 我们提出了一个多尺度统计模型,用于在高对比度下损坏多变量图像系列的滋扰组件。 集成到一个可学习的架构中,它利用了问题的物理学,并以检测信噪比方面的最佳方式实现了同一颗恒星的多次观测的融合。 该方法应用于VLT / SPHERE仪器的数据,显着提高了检测灵敏度和星光度和光度估计的准确性。
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 inst...