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Data · dataset · 2026

Removal of spectro-polarimetric fringes by two-dimensional principal component analysis

Listed in National Center for Atmospheric Research

We investigate the application of two-dimensional Principal Component Analysis (2D PCA) to the problem of removal of polarization fringes from spectro-polarimetric data sets.

Description

We show how the transformation of the PCA basis through a series of carefully chosen rotations allows us 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 defringing 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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National Center for Atmospheric Research2867076b-71b3-4710-9dbd-061c3cd464c99 d agoJSON v1
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concepts[field].local:field:earth-environmentalmapping · data ucar educonnector:data_ucar_edu@1.0.0
concepts[field].local:field:ocean-atmosphericmapping · data ucar educonnector:data_ucar_edu@1.0.0
created_datesource · data ucar educonnector:data_ucar_edu@1.0.0
descriptionsource · data ucar educonnector:data_ucar_edu@1.0.0/notes
publication_datesource · data ucar educonnector:data_ucar_edu@1.0.0
titlesource · data ucar educonnector:data_ucar_edu@1.0.0/title
updated_datesource · data ucar educonnector:data_ucar_edu@1.0.0