Parallel and perpendicular alignments of velocity gradient and magnetic field observed in the molecular clouds L1478 and L1482

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ABSTRACT Star formation is a complex process that typically occurs in dense regions of molecular clouds mainly regulated by magnetic fields, magnetohydrodynamic (MHD) turbulence, and self-gravity. However, it remains a challenging endeavour to trace the magnetic field and determine regions of gravitational collapse, where the star is forming. Based on the anisotropic properties of MHD turbulence, a new technique termed velocity gradient technique (VGT) has been proposed to address these challenges. In this study, we apply the VGT to two regions of the giant California Molecular Cloud (CMC), namely, L1478 and L1482, and analyse the difference in their physical properties. We use the 12CO (J = 2–1), 13CO (J = 2–1), and C18O (J = 2–1) emission lines observed with the Heinrich Hertz Submillimeter Telescope. We compare VGT results calculated in the resolutions of 3.3 and 10 arcmin to Planck polarization at 353 GHz and 10 arcmin to determine areas of MHD turbulence dominance and self-gravity dominance. We show that the resolution difference can introduce misalignment between the two measurements. We find the VGT-measured magnetic fields globally agree with those from Planck in L1478, suggesting self-gravity’s effect is insignificant. The best agreement appears in VGT-12CO. As for L1482, the VGT measurements are statistically perpendicular to the Planck polarization indicating the dominance of self-gravity. This perpendicular alignment is more significant in VGT-13CO and VGT-C18O.

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Probing three-dimensional magnetic fields: II – an interpretable Convolutional Neural Network
  • Dec 23, 2023
  • Monthly Notices of the Royal Astronomical Society
  • Yue Hu + 3 more

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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Tracing of the magnetic field with Velocity Gradient Technique (VGT) allows observers to probe magnetic field directions with spectroscopic data. In this paper, we employ the method of Principal Component Analysis (PCA) to extract the spectroscopic information most valuable for VGT. By using synthetic observation data from numerical simulations, we show that PCA acts in a way similar to spatial filtering along the velocity axis. We study both subsonic and supersonic simulations and show that with the PCA filtering the tracing of magnetic fields by the VGT is significantly improved. Using 21 cm GALFA data, we demonstrate that the PCA filtering improves the alignment of the velocity gradients and the Planck dust polarization.

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