  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.
