论文标题
物理限制的因果噪声模型，用于外部行星的高对比度成像
Physically constrained causal noise models for high-contrast imaging of exoplanets
论文作者
论文摘要
在高对比度成像（HCI）数据中检测外部行星的数据取决于后处理方法，从而从宿主恒星中删除伪光。到目前为止，此任务的现有方法几乎无法明确利用有关该问题的任何可用域知识。我们提出了一种基于修改后的半兄弟回归方案的HCI后处理方法的新方法，并展示了我们如何使用此框架将机器学习与现有科学领域知识相结合。在三个真实的数据集中，我们证明所得系统的性能明显比当前领先的算法之一（在视觉上和SNR方面）表现更好。如果进一步的研究能够确认这些结果，我们的方法可能有可能在新的和档案数据中允许大量发现系外行星。
The detection of exoplanets in high-contrast imaging (HCI) data hinges on post-processing methods to remove spurious light from the host star. So far, existing methods for this task hardly utilize any of the available domain knowledge about the problem explicitly. We propose a new approach to HCI post-processing based on a modified half-sibling regression scheme, and show how we use this framework to combine machine learning with existing scientific domain knowledge. On three real data sets, we demonstrate that the resulting system performs clearly better (both visually and in terms of the SNR) than one of the currently leading algorithms. If further studies can confirm these results, our method could have the potential to allow significant discoveries of exoplanets both in new and archival data.
