行星探测下降到几个$λ$ /D：PSF减法的RSDI/TLOCI方法
Planet detection down to a few $λ$/D: an RSDI/TLOCI approach to PSF subtraction
摘要：
大多数当前的高对比度成像点扩散函数（PSF）减法算法使用某种形式的最小二乘噪声最小化来找到系外行星，在后处理之前，通常隐藏在仪器斑点噪声之下。在当前标准PSF减法算法中，使用角度和/或同时光谱差分成像（分别为ADI、SSDI）从目标图像序列导出一组参考图像以减去每个目标图像。然而，为了避免过多的系外行星自减影，ADI和SSDI（在没有强光谱特征的情况下）严格限制了小分离处的参考图像的可用数量。这限制了最小二乘算法的性能，导致在小角度分离时对系外行星的灵敏度较低。可能的解决方案是通过获取更长的序列来使用额外的参考图像，如果预计系外行星将显示出强烈的光谱特征，则使用SSDI，或者使用在其他目标上获取的图像。后一种选择称为参考星差分成像，它依赖于使用与目标图像高度相关的参考图像，在以前的地面高对比度成像调查中效果不佳。我们提出的工作结果，以优化PSF减法与GPIES参考库使用最小二乘算法，旨在最大限度地减少斑点噪声和最大限度地提高行星吞吐量，从而最大限度地提高行星信噪比（SNR）。使用2014年12月51 Eri GPI数据，在内部100 mas到300 mas环中，我们发现当使用RSDI和/或我们的优化方案时，SNR没有明显的改善。这一结果虽然仍在研究中，但似乎表明ADI+SSDI数据集上的当前算法得到了优化，并且通过使用PSF存档可以实现有限的增益。
英文摘要：
Most current high contrast imaging point spread function (PSF) subtraction algorithms use some form of a least-squares noise minimization to find exoplanets that are, before post-processing, often hidden below the instrumental speckle noise. In the current standard PSF subtraction algorithms, a set of reference images is derived from the target image sequence to subtract each target image, using Angular and/or Simultaneous Spectral Differential Imaging (ADI, SSDI, respectively). However, to avoid excessive exoplanet self-subtraction, ADI and SSDI (in the absence of a strong spectral feature) severely limit the available number of reference images at small separations. This limits the performance of the least-squares algorithm, resulting in lower sensitivity to exoplanets at small angular separations. Possible solutions are to use additional reference images by acquiring longer sequences, use SSDI if the exoplanet is expected to show strong spectral features, or use images acquired on other targets. The latter option, known as Reference Star Differential Imaging (RSDI), which relies on the use of reference images that are highly correlated to the target image, has been ineffective in previous ground-based high contrast imaging surveys. We present the results of work to optimize PSF subtraction with the GPIES reference library using a least-squares algorithm designed to minimize speckle noise and maximize planet throughput, thus maximizing the planet signal to noise ratio (SNR). Using December 2014 51 Eri GPI data in the inner 100 mas to 300 mas annulus, we find no apparent improvement in SNR when using RSDI and/or our optimization scheme. This result, while still being investigated, seems to show that current algorithms on ADI+SSDI data sets are optimized, and that limited gains can be achieved by using a PSF archive.
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