Application of Convolutional Neural Networks to Predict Magnetic Fields Directions in Turbulent Clouds

TL;DR

This paper demonstrates that a deep learning model, CASI-3D, can accurately predict magnetic field orientations in turbulent molecular clouds from line emission data, outperforming traditional methods in resolution.

Contribution

The study introduces a novel application of CASI-3D to infer magnetic field directions from molecular line emission, achieving high accuracy and higher resolution than existing polarization maps.

Findings

CASI-3D predicts magnetic field directions with less than 10° error in sub-Alfvenic clouds.

The method produces a magnetic field map of Taurus with three times higher resolution than Planck.

Predictions are consistent with Planck dust polarization measurements.

Abstract

We adopt the deep learning method CASI-3D (Convolutional Approach to Structure Identification-3D) to infer the orientation of magnetic fields in sub-/trans- Alfvenic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code RADMC-3d to model 12CO and 13CO (J = 1-0) line emission from the simulated clouds and then train a CASI-3D model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained CASI-3D model is able to infer magnetic field directions with low error (< 10deg for sub-Alfvenic samples and <30deg for trans-Alfvenic samples). We furthermore test the performance of CASI-3D on a real sub-/trans- Alfvenic region in Taurus. The CASI-3D prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three-times higher angular resolution than the Planck map.