4.7 Article

Velocity gradients: magnetic field tomography towards the supernova remnant W44

期刊

出版社

OXFORD UNIV PRESS
DOI: 10.1093/mnras/stab3783

关键词

MHD; turbulence; ISM: general; ISM: magnetic fields; ISM: supernovae remnants

资金

  1. NASA [TCAN 144AAG1967, ATP AAH7546]
  2. NSF [AST 1715754]

向作者/读者索取更多资源

This study verifies the effectiveness of the velocity gradient technique (VGT) in a broader context by applying it to a molecular cloud interacting with the supernova remnant (SNR) W44. The VGT accurately measures the magnetic fields, especially in intense molecular gas emission regions, and shows good agreement with the Planck polarization. However, there is a misalignment between the VGT and Planck measurements in low-intensity molecular gas areas due to the foreground contribution to the polarization.
As a novel approach for tracing interstellar magnetic fields, the velocity gradient technique (VGT) has been proven to be effective for probing magnetic fields in the diffuse interstellar medium (ISM). In this work, we verify the VGT in a broader context by applying the technique to a molecular cloud interacting with the supernova remnant (SNR) W44. We probe the magnetic fields with the VGT using CO, HCO+ and H i emission lines and make a comparison with the Planck 353-GHZ dust polarization. We show that the VGT gives an accurate measurement that coheres with the Planck polarization especially in intense molecular gas emission regions. We further study the foreground's contribution on the polarization that results in misalignment between the VGT and the Planck measurements in low-intensity molecular gas areas. We advance the VGT to achieve magnetic field tomography by decomposing the SNR W44 into various velocity components. We show that W44's velocity component at v similar to 45 km s(-1) exhibits the largest coverage and gives best agreement with Planck polarization in terms of magnetic field orientation.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据