Characterizing 3D Magnetic Fields and Turbulence in H I Clouds

TL;DR

This paper presents a deep learning framework trained on simulations to predict the 3D magnetic field structure and turbulence parameters in H I clouds from spectroscopic data, validated with observational data and existing techniques.

Contribution

Introduces a novel deep learning method to infer 3D magnetic fields and turbulence properties from H I observations, integrating simulations and real data.

Findings

Deep learning accurately predicts 3D magnetic field orientation and strength.

Predicted magnetic field angles agree with velocity gradient and Planck polarization data.

Method effectively maps turbulence parameters like sonic and Alfvén Mach numbers.

Abstract

3D Galactic magnetic fields are critical for understanding the interstellar medium, Galactic foreground polarization, and the propagation of ultra-high-energy cosmic rays. Leveraging recent theoretical insights into anisotropic magnetohydrodynamic (MHD) turbulence, we introduce a deep learning framework to predict the full 3D magnetic field structure-including the plane-of-sky (POS) position angle, line-of-sight (LOS) inclination, magnetic field strength, sonic Mach number ($M_s$), and Alfv\'en Mach number ($M_A$)-from spectroscopic H~I observations. The deep learning model is trained on synthetic H~I emission data generated from multiphase 3D MHD simulations. We then apply the trained model to observational data from the Commensal Radio Astronomy FAST Survey, presenting maps of 3D magnetic field orientation, magnetic field strength, $M_s$, and $M_A$ for two H~I clouds, a low-velocity cloud (LVC) and an intermediate-velocity cloud (IVC), which overlap in the POS yet reside at different LOS distances. The deep-learning-predicted POS magnetic field position angles align closely with those determined using the velocity gradient technique, whose integrated results are consistent with independent measurements from Planck 353~GHz polarization data. This study demonstrates the potential of deep learning approaches as powerful tools for modeling the 3D distributions of 3D Galactic magnetic fields and turbulence properties throughout the Galaxy.