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rdac:C10001 bibo:Article

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n8:sunatmosphere n8:sunmagnetictopology n8:sunsurfacemagnetism n8:techniquesspectroscopic n8:techniqueshighangularresolution n18:thesun n8:methodsobservational

dcterms:title

Magnetic field strength distribution of magnetic bright points inferred from filtergrams and spectro-polarimetric data

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2013-01-01

rdaw:P10072

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n2:kuhnero n9:id n2:mullerr n17:person n2:hanslmeiera n19:person

dcterms:abstract

Context. Small scale magnetic fields can be observed on the Sun in G-band filtergrams as magnetic bright points (MBPs) or identified in spectro-polarimetric measurements due to enhanced signals of Stokes profiles. These magnetic fields and their dynamics play a crucial role in understanding the coronal heating problem and also in surface dynamo models. MBPs can theoretically be described to evolve out of a patch of a solar photospheric magnetic field with values below the equipartition field strength by the so-called convective collapse model. After the collapse, the magnetic field of MBPs reaches a higher stable magnetic field level. Aims. The magnetic field strength distribution of small scale magnetic fields as seen by MBPs is inferred. Furthermore, we want to test the model of convective collapse and the theoretically predicted stable value of about 1300 G. Methods. We used four different data sets of high-resolution Hinode/SOT observations that were recorded simultaneously with the broadband filter device (G-band, Ca II-H) and the spectro-polarimeter. To derive the magnetic field strength distribution of these small scale features, the spectropolarimeter (SP) data sets were treated by the Merlin inversion code. The four data sets comprise different solar surface types: active regions (a sunspot group and a region with pores), as well as quiet Sun. Results. In all four cases the obtained magnetic field strength distribution of MBPs is similar and shows peaks around 1300 G. This agrees well with the theoretical prediction of the convective collapse model. The resulting magnetic field strength distribution can be fitted in each case by a model consisting of log-normal components. The important parameters, such as geometrical mean value and multiplicative standard deviation, are similar in all data sets, so only the relative weighting of the components is different.

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aa16894-11

dcterms:dateCopyrighted

2013-01-01

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© ESO, 2013

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ESO

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