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Pattern recognition techniques in Polarimetry

Published online by Cambridge University Press:  24 July 2015

Arturo López Ariste
Arturo López Ariste*
Affiliation:
IRAP, Toulouse, France email: Arturo.LopezAriste@irap.omp.eu
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Abstract

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Sparsity is a property of data by which it can be represented using a small number of patterns. It is the key concept behind an evergrowing list of mathematical techniques for handling data and recover from it signals or information in conditions previously thought impossible. The application of those techniques to spectropolarimetric data is relatively straightforward. We present three examples of such application: the use of Principal Component Analysis to invert the magnetic field in solar prominences from spectropolarimetry of the He D3 line, the removal of fringes from spectropolarimetric data with Relevance Vector Machines, and the retrieval of high resolution spectra from low resolution data with Compressed Sensing.

Keywords

methods: data analysispolarization
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S305: Polarimetry: From the Sun to Stars and Stellar Environments , December 2014 , pp. 207 - 215
Copyright
Copyright © International Astronomical Union 2015 

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