Abstract
We present a pattern-recognition based approach to the problem of removal of polarized fringes from spectro-polarimetric data. We demonstrate that 2D Principal Component Analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us in principle to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.
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