用深度学习提升高对比度系外行星成像的探测与参数估计能力
Deep learning for exoplanet detection and characterization by direct imaging at high contrast
- 构建多尺度统计模型，建模成像中的干扰噪声
- 在视向速度数据上提升信噪比，显著增强探测灵敏度
- 适合天体物理与机器学习交叉研究者参考
系外行星直接成像面临高角分辨率与高对比度的挑战。本文提出一种多尺度统计模型，用于刻画高对比度下多变量图像序列中的冗余成分。该模型嵌入可学习架构中，融合了物理先验知识，实现对同一恒星多帧观测数据的最优融合，从而最大化检测信噪比。应用于甚大望远镜/SPHERE仪器的数据，方法显著提升了系外行星的探测灵敏度，以及天体位置和光度估计的准确性。
原文摘要 · Abstract (English)
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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