北落师门日冕图像的优化主成分分析
Optimized Principal Component Analysis on Coronagraphic Images of the Fomalhaut System
摘要：
我们提出了一项研究的结果，以优化的主成分分析（PCA）算法的行星检测，一种新的算法补充ADI和LOCI，以增加对比度，可实现旁边的一个明亮的恒星。恒星PSF是通过去除主成分的线性组合来构建的，允许来自太阳系外行星的通量照射。所使用的主成分的数量决定了恒星PSF的全局建模程度。使用更多的主成分可能会减少最终图像中的斑点数量，但也会增加背景噪声。我们将PCA应用于在4.05微米处用切趾相位板获得的北落师门VLT NaCo图像。在4-10 AU范围内，没有发现伴星，质量上限为13-18 M_Jup。PCA实现更高的灵敏度比LOCI算法的北落师门日冕数据高达1级。我们对PCA代码进行了几次调整，并确定其中哪些被证明是最有效的，可以最大限度地提高来自非常接近其母星的行星的信噪比。我们证明，优化PCA中使用的主成分的数量被证明是最有效的拉出行星信号。
英文摘要：
We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing ADI and LOCI for increasing the contrast achievable next to a bright star. The stellar PSF is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modelled. Using more principal components may decrease the number of speckles in the final image, but also increases the background noise. We apply PCA to Fomalhaut VLT NaCo images acquired at 4.05 micron with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M_Jup from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 magnitude. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.
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