ABSTRACT Observing 3D magnetic fields, including orientation and strength, within the interstellar medium is vital but notoriously difficult. However, recent advances in our understanding of anisotropic magnetohydrodynamic (MHD) turbulence demonstrate that MHD turbulence and 3D magnetic fields leave their imprints on the intensity features of spectroscopic observations. Leveraging these theoretical frameworks, we propose a novel Convolutional Neural Network (CNN) model to extract this embedded information, enabling the probe of 3D magnetic fields. This model examines the plane-of-the-sky magnetic field orientation (ϕ), the magnetic field’s inclination angle (γ) relative to the line-of-sight, and the total magnetization level (M$$_{\rm A}^{-1}$$) of the cloud. We train the model using synthetic emission lines of 13CO (J = 1–0) and C18O (J = 1–0), generated from 3D MHD simulations that span conditions from sub-Alfvénic to super-Alfvénic molecular clouds. Our tests confirm that the CNN model effectively reconstructs the 3D magnetic field topology and magnetization. The median uncertainties are under 5° for both ϕ and γ, and less than 0.2 for MA in sub-Alfvénic conditions (MA ≈ 0.5). In super-Alfvénic scenarios (MA ≈ 2.0), they are under 15° for ϕ and γ, and 1.5 for MA. We applied this trained CNN model to the L1478 molecular cloud. Results show a strong agreement between the CNN-predicted magnetic field orientation and that derived from Planck 353 GHz polarization. The CNN approach enabled us to construct the 3D magnetic field map for L1478, revealing a global inclination angle of ≈76° and a global MA of ≈1.07.
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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.
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- Award ID(s):
- 2009674
- PAR ID:
- 10569824
- 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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