新模型用多尺度方法分离恒星杂波，提升系外行星探测精度。
A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations
- 基于物理对称性和频谱联合表示建模恒星光斑噪声
- 在SPHERE/VLT数据上显著优化精确率-召回率平衡
- 适合大规模巡天，计算高效且对数据质量不敏感
系外行星搜寻是天文学热点领域，直接成像因信号微弱且被强残余星光掩盖而极具挑战。本文提出一种新型统计模型，通过多尺度方法捕捉干扰波动，利用问题对称性与基于物理原理的联合频谱通道表示。该模型嵌入可解释的端到端可学习框架，实现系外行星检测与通量估计的同步完成。算法在甚大望远镜（VLT）上的SPHERE仪器数据集上评估，显著改善了精确率-召回率权衡，在原本难以处理的挑战性数据集上表现突出。所提方法计算效率高、对不同数据质量鲁棒，适用于大规模观测巡天。
原文摘要 · Abstract (English)
The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles. Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation. The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT). It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers. The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.
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