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arXiv 1811.03211astro-ph.IM
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二维PCA消除光谱偏振条纹

Removal of Spectro-Polarimetric Fringes by 2D PCA

Roberto Casini, Wenxian Li

摘要:

研究了二维主成分分析(2DPCA)在光谱偏振数据中偏振条纹去除问题中的应用。我们展示了如何通过一系列精心选择的旋转变换的PCA基础允许限制偏振条纹(和其他固定的仪器效应)的基础“矢量”,这在同一时间在很大程度上是缺乏从所观察到的目标的光谱信号的减少。可以设计用于确定PCA基的最佳旋转系列的算法,从而打开使光谱偏振数据集的去边缘化的过程自动化的可能性。我们比较所提出的方法与更传统的傅立叶滤波的斯托克斯光谱的性能。

英文摘要:

We investigate the application of 2-dimensional Principal Component Analysis (2D PCA) to the problem of removal of polarization fringes from spectro-polarimetric data sets. We show how the transformation of the PCA basis through a series of carefully chosen rotations allows to confine polarization fringes (and other stationary instrumental effects) to a reduced set of basis "vectors", which at the same time are largely devoid of the spectral signal from the observed target. It is possible to devise algorithms for the determination of the optimal series of rotations of the PCA basis, thus opening the possibility of automating the procedure of de-fringing of spectro-polarimetric data sets. We compare the performance of the proposed method with the more traditional Fourier filtering of Stokes spectra.

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