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Title: Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds
Abstract We adopt the deep learning methodcasi-3d(convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic 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 coderadmc-3dto model12CO and13CO (J = 1−0) line emission from the simulated clouds and then train acasi-3dmodel on these line emission data cubes to predict magnetic field morphology at the pixel level. The trainedcasi-3dmodel is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance ofcasi-3don a real sub-/trans- Alfvénic region in Taurus. Thecasi-3dprediction 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.  more » « less
Award ID(s):
2009674
PAR ID:
10569824
Author(s) / Creator(s):
; ;
Publisher / Repository:
IOP
Date Published:
Journal Name:
The Astrophysical Journal
Volume:
942
Issue:
2
ISSN:
0004-637X
Page Range / eLocation ID:
95
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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