Application of Convolutional Neural Networks to Predict Magnetic Fields Directions in Turbulent Clouds by Duo Xu et al. on Tuesday 29 November

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.

arXiv: http://arxiv.org/abs/http://arxiv.org/abs/2211.14266v1