Dataset for "Helicity proxies from linear polarisation of solar active regions"

Dataset

Description

The α effect is believed to play a key role in the generation of the solar magnetic field. A fundamental test for its significance in the solar dynamo is to look for magnetic helicity of opposite signs in the two hemispheres, and at small and large scales. However, measuring magnetic helicity is compromised by the inability to fully infer the magnetic field vector from observations of solar spectra, caused by what is known as the π ambiguity of spectropolarimetric observations. We decompose linear polarisation into parity-even and parity-odd E and B polarisations, which are not affected by the π ambiguity. Furthermore, we study whether the correlations of spatial Fourier spectra of B and parity-even quantities such as E or temperature T are a robust proxy for magnetic helicity of solar magnetic fields. We analyse polarisation measurements of active regions observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics observatory. Theory predicts the magnetic helicity of active regions to have, statistically, opposite signs in the two hemispheres. We then compute the parity-odd EB and TB correlations, and test for systematic preference of their sign based on the hemisphere of the active regions. We find that: (i) EB and TB correlations are a reliable proxy for magnetic helicity, when computed from linear polarisation measurements away from spectral line cores, and (ii) E polarisation reverses its sign close to the line core. Our analysis reveals Faraday rotation to not have a significant influence on the computed parity-odd correlations. The EB decomposition of linear polarisation appears to be a good proxy for magnetic helicity independent of the π ambiguity. This allows us to routinely infer magnetic helicity directly from polarisation measurements.

The full article can be found at https://arxiv.org/abs/2001.10884

License (for files): Creative Commons Attribution 4.0 International
Date made available10 Jun 2020
PublisherZenodo

Dataset Licences

  • CC-BY-4.0

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