Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer
Measuring the 3D spatial distribution of magnetic fields in the interstellar medium and the intracluster medium is crucial yet challenging. The probing of the 3D magnetic field’s 3D distribution, including the field plane-of-sky orientation ( ψ ), the magnetic field’s inclination angle ( γ ) relativ...
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2025-01-01
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| Online Access: | https://doi.org/10.3847/1538-4357/adaf97 |
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| author | Yue Hu Alex Lazarian |
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| description | Measuring the 3D spatial distribution of magnetic fields in the interstellar medium and the intracluster medium is crucial yet challenging. The probing of the 3D magnetic field’s 3D distribution, including the field plane-of-sky orientation ( ψ ), the magnetic field’s inclination angle ( γ ) relative to the line of sight, and the magnetization (∼the inverse Alfvén Mach number ${M}_{{\rm{A}}}^{-1}$ ), at different distances from the observer makes the task even more formidable. However, the anisotropy and Faraday decorrelation effect in polarized synchrotron emission offer a unique solution. We show that due to the Faraday decorrelation, only regions up to a certain effective path length along the line of sight contribute to the statistical correlation of the measured polarization. The 3D spatial information can be consequently derived from synchrotron polarization derivatives (SPDs), which are calculated from the difference in synchrotron polarization across two wavelengths. We find that the 3D magnetic field can be estimated from the anisotropy observed in SPDs: the elongation direction of the SPD structures probes ψ , and the degree of SPD anisotropy, along with its morphological curvature, provides insights into ${M}_{{\rm{A}}}^{-1}$ and γ . To extract these anisotropic features and their correlation with the 3D magnetic field, we propose utilizing a machine learning approach, specifically the Vision Transformer (ViT) architecture, which was exemplified by the success of ChatGPT. We train the ViT using synthetic synchrotron observations generated from magnetohydrodynamic turbulence simulations in sub-Alfvénic and super-Alfvénic conditions. We show that ViT’s application to multiwavelength SPDs can successfully reconstruct the 3D magnetic fields’ 3D spatial distribution. |
| format | Article |
| id | doaj-art-131bf77f5cb441ee8bf5f09f80246104 |
| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| spelling | doaj-art-131bf77f5cb441ee8bf5f09f802461042025-08-20T02:17:24ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-0198115810.3847/1538-4357/adaf97Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision TransformerYue Hu0https://orcid.org/0000-0002-8455-0805Alex Lazarian1https://orcid.org/0000-0002-7336-6674Institute for Advanced Study , 1 Einstein Drive, Princeton, NJ 08540, USA ; yuehu@ias.eduDepartment of Astronomy, University of Wisconsin–Madison , Madison, WI 53706, USA ; lazarian@astro.wisc.eduMeasuring the 3D spatial distribution of magnetic fields in the interstellar medium and the intracluster medium is crucial yet challenging. The probing of the 3D magnetic field’s 3D distribution, including the field plane-of-sky orientation ( ψ ), the magnetic field’s inclination angle ( γ ) relative to the line of sight, and the magnetization (∼the inverse Alfvén Mach number ${M}_{{\rm{A}}}^{-1}$ ), at different distances from the observer makes the task even more formidable. However, the anisotropy and Faraday decorrelation effect in polarized synchrotron emission offer a unique solution. We show that due to the Faraday decorrelation, only regions up to a certain effective path length along the line of sight contribute to the statistical correlation of the measured polarization. The 3D spatial information can be consequently derived from synchrotron polarization derivatives (SPDs), which are calculated from the difference in synchrotron polarization across two wavelengths. We find that the 3D magnetic field can be estimated from the anisotropy observed in SPDs: the elongation direction of the SPD structures probes ψ , and the degree of SPD anisotropy, along with its morphological curvature, provides insights into ${M}_{{\rm{A}}}^{-1}$ and γ . To extract these anisotropic features and their correlation with the 3D magnetic field, we propose utilizing a machine learning approach, specifically the Vision Transformer (ViT) architecture, which was exemplified by the success of ChatGPT. We train the ViT using synthetic synchrotron observations generated from magnetohydrodynamic turbulence simulations in sub-Alfvénic and super-Alfvénic conditions. We show that ViT’s application to multiwavelength SPDs can successfully reconstruct the 3D magnetic fields’ 3D spatial distribution.https://doi.org/10.3847/1538-4357/adaf97Astrophysical magnetismMagnetohydrodynamicsInterstellar synchrotron emissionConvolutional neural networks |
| spellingShingle | Yue Hu Alex Lazarian Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer The Astrophysical Journal Astrophysical magnetism Magnetohydrodynamics Interstellar synchrotron emission Convolutional neural networks |
| title | Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer |
| title_full | Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer |
| title_fullStr | Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer |
| title_full_unstemmed | Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer |
| title_short | Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer |
| title_sort | probing three dimensional magnetic fields iv synchrotron polarization derivative and vision transformer |
| topic | Astrophysical magnetism Magnetohydrodynamics Interstellar synchrotron emission Convolutional neural networks |
| url | https://doi.org/10.3847/1538-4357/adaf97 |
| work_keys_str_mv | AT yuehu probingthreedimensionalmagneticfieldsivsynchrotronpolarizationderivativeandvisiontransformer AT alexlazarian probingthreedimensionalmagneticfieldsivsynchrotronpolarizationderivativeandvisiontransformer |