arXiv 1912.012322019-12-03astro-ph.EPastro-ph.IM

${\tt PlanetEvidence}$：行星还是噪音？

${\tt PlanetEvidence}$: Planet or Noise?

Jacob Golomb, Graça Rocha, Tiffany Meshkat, Michael Bottom, Dimitri Mawet, Bertrand Mennesson, Gautam Vasisht, Jason Wang

备注：: 29 pages, 23 figures and 3 tables, submitted to ApJ

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摘要：

这里提出的工作试图回答这个问题：我们如何决定当一个给定的adetection是一个行星或只是残留的噪声在系外行星直接成像数据？为此，我们提出了一种在贝叶斯框架内实现的方法：（1）将“源检测”和“源表征”统一到一个单一的严格的数学框架中;（2）在给定数据的S/N的情况下进行充分的假设检验;（3）在存在仪器和背景噪声的情况下提高行星微弱信号的可检测性，并优化行星的表征。作为概念证明，我们实现了一个名为${\tt PlanetEvidence}$的例程，该例程将嵌套采样技术（Multinest）与后处理技术（Karhunen-Loeve图像处理（KLIP）算法）集成在一起。这是将这种后处理方法转换为完全贝叶斯观点的第一步。我们测试我们的方法对真正的直接成像数据，特别是使用GPI数据的$β$ Picardium b，和合成数据。我们发现，对于前者，该方法强烈倾向于行星的存在（如预期的那样），并恢复真实的参数后验分布。而对于后一种情况，我们的方法使我们能够检测到（真正的）暗淡的来源不可见的肉眼作为真正的行星，而不是背景噪音，并设置一个新的较低的阈值检测在2 $σ$水平近似。此外，它还允许我们量化我们的信心，即给定的探测是一个真实的行星，而不仅仅是残余噪声（例如残余斑点）。下一个自然的步骤是扩展这种方法来构建一个基于贝叶斯的盲检测算法，也就是说，不需要对行星的位置进行初始猜测。这是正在进行的工作的主题。

英文摘要：

The work presented here attempts at answering the question: how do we decide when a given adetection is a planet or just residual noise in exoplanet direct imaging data? To this end we present a method implemented within a Bayesian framework: (1) to unify 'source detection', and, 'source characterization' into one single rigorous mathematical framework; (2) to enable an adequate hypothesis testing given the S/N of the data; (3) to enhance the detectability of planets faint signal in the presence of instrumental and background noise and to optimize the characterization of the planet. As a proof of concept we implemented a routine named ${\tt PlanetEvidence}$ that integrates the nested sampling technique (Multinest) with a post-processing technique, the Karhunen-Loeve Image Processing (KLIP), algorithm. This is a first step to recast such post-processing method into a fully Bayesian perspective. We test our approach on real direct imaging data, specifically using GPI data of $β$ Pictoris b, and, on synthetic data. We find that for the former the method strongly favors the presence of a planet (as expected) and recovers the true parameter posterior distributions. While for the latter case our approach allows us to detect (true) dim sources invisible to the naked eye as real planets, rather than background noise, and set a new lower threshold for detection at the 2$σ$ level approximately. Further it allows us to quantify our confidence that a given detection is a real planet and not just residual noise (for example residual speckles). The next natural step is to extend this approach to construct a Bayesian-based algorithm for blind detection, that is, not requiring an initial guess as to the location of the planet. This is the subject of ongoing work.

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