Solar energy paper index
Horizontal Magnetic Fields Dominate the Quiet Sun Internetwork in Sunrise III Observations. Evidence from Traditional and Transformer-Based Inversions
One-line summary
A solar energy research paper on Horizontal Magnetic Fields Dominate the Quiet Sun Internetwork in Sunrise III Observations. Evidence from Traditional and Transformer-Based Inversions.
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Chinese explanation / 中文解读
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Original abstract
The relative prevalence of horizontal and vertical magnetic fields in the quiet-Sun internetwork remains debated, owing to the weak linear polarisation signals that make the inferred magnetic inclination distributions sensitive to observational quality and inversion methodology. We investigate the magnetic topology of the quiet-Sun internetwork in {\sc Sunrise~iii} observations by independently applying transformer-based and traditional inversion techniques to the same spectropolarimetric dataset. We analyse observations obtained with the Sunrise Chromospheric Infrared spectroPolarimeter (SCIP). Transformer-based inversions (SINN), trained exclusively on synthetic Stokes profiles generated from a large radiative magnetohydrodynamic simulation, are compared with independent inversions performed using the \textsc{DeSIRe} code. Both inversion methods recover a quiet-Sun magnetic topology in which internetwork fields are strongly dominated by the horizontal component, whereas network fields exhibit a substantially more balanced topology. At $\logτ=-1.2$, the median horizontal and unsigned line-of-sight field components in the internetwork are 64 and 7G with \textsc{DeSIRe}, and 71 and 8G with SINN, respectively, corresponding to a horizontal-to-line-of-sight ratio of approximately nine for both methods. The agreement between the two fundamentally different inversion methodologies indicates that the quiet-Sun internetwork is characterised by a substantially stronger horizontal than vertical magnetic field component. These results demonstrate that transformer-based inversions can be successfully transferred from synthetic training data to real spectropolarimetric observations, while providing inference orders of magnitude faster than traditional inversion techniques.
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