Probing Three-Dimensional Magnetic Fields: IV -- Synchrotron Polarization Derivative and Vision Transformer

TL;DR

This paper introduces a novel method combining synchrotron polarization derivatives with Vision Transformer machine learning to reconstruct three-dimensional magnetic fields in space, leveraging synthetic MHD turbulence data.

Contribution

It proposes a new approach that uses SPD anisotropy and a Vision Transformer to accurately infer 3D magnetic field structures from polarized synchrotron emission.

Findings

ViT successfully reconstructs 3D magnetic fields from synthetic data.

SPD anisotropy correlates with magnetic field orientation and strength.

Method demonstrates potential for 3D magnetic field mapping in astrophysics.

Abstract

Measuring the 3D spatial distribution of magnetic fields in the interstellar medium and the intracluster medium is crucial yet challenging. The probing of 3D magnetic field's 3D distribution, including the field plane-of-sky orientation ($\psi$), the magnetic field's inclination angle ($\gamma$) relative to the line of sight, and magnetization ($\sim$ the inverse Alfv\'en Mach number $M_A^{-1}$), at different distances from the observer makes the task even more formidable. However, the anisotropy and Faraday decorrelation effect in polarized synchrotron emission offers 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 SPD: the elongation direction of the SPD structures probes $\psi$ and the degree of SPD anisotropy, along with its morphological curvature, provides insights into $M_A^{-1}$ and $\gamma$. 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 the ChatGPT. We train the ViT using synthetic synchrotron observations generated from MHD turbulence simulations in sub-Alfv\'enic and super-Alfv\'enic conditions. We show that ViT's application to multi-wavelength SPDs can successfully reconstruct the 3D magnetic fields' 3D spatial distribution.