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arXiv 1602.08381astro-ph.IMastro-ph.EP
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直接成像ADI序列中用于系外行星探测的低秩加稀疏分解。LLSG算法

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

C. A. Gomez Gonzalez, O. Absil, P. -A. Absil, M. Van Droogenbroeck, D. Mawet, J. Surdej

发表信息：
A&A 589, A54 (2016)
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摘要：

数据处理是高对比度系外行星成像的关键组成部分。它的作用几乎与日冕仪或波前控制系统的选择一样重要，并且与所选择的观测策略交织在一起。在用于角微分成像（ADI）的数据处理技术中，最近的是基于主成分分析（PCA）的算法族。在这种情况下，PCA用作子空间投影技术，用于构建参考点扩散函数（PSF），该参考点扩散函数可以从科学数据中减去，以提高数据中存在的潜在同伴的可检测性。不幸的是，当从科学数据本身构建这个参考PSF时，PCA具有某些限制，例如低维正交子空间对非高斯噪声的敏感性。受机器学习算法（如鲁棒PCA）的最新进展的启发，我们的目标是提出一种局部子空间投影技术，该技术在近实时速度下同伴的可检测性方面超过当前基于PCA的后处理算法，这种质量将对未来的直接成像调查有用。我们使用机器学习文献中最近提出的随机低秩近似方法，再加上条目式阈值处理，将ADI图像序列局部分解为低秩、稀疏和高斯噪声分量（LLSG）。这种局部三项分解将星光和相关的斑点噪声从行星信号中分离出来，行星信号主要保留在稀疏项中。我们测试了我们的新算法的性能上获得的一个长ADI序列的Beta Picardiac与VLT/NACO。与标准PCA方法相比，LLSG分解达到了更高的信噪比，并且在接收机操作特征空间中具有整体更好的性能。（节略）。

英文摘要：

Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on beta Pictoris with VLT/NACO. Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. (abridged).

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