论文标题
用梯度技术探测星系簇中的磁场形态
Probing Magnetic Field Morphology in Galaxy Clusters with the Gradient Technique
论文作者
论文摘要
簇内介质(ICM)中的磁场会影响星系簇的结构和演变。但是,它们的性质在很大程度上是未知的,并且测量星系簇中的磁场具有挑战性,尤其是在单个无线电源之外的大规模上。在这项工作中,我们使用强度梯度探测了簇中磁场的天平方向。该技术是采用湍流气体发射强度图的梯度技术(GT)的分支。我们利用了Perseus,M 87,Coma和A2597 Galaxy簇的Chandra X射线图像,以及来自Perseus的同步器发射的VLA无线电观察结果。我们发现这些田地主要跟随珀尔修斯(Perseus)的宽臂,这与数值模拟一致。 GT预测的磁场显示出磁性悬垂的特征,周围是由凉爽核心簇中心的超级质量黑洞(SMBH)驱动的上升气泡,以及围绕与昏迷集群合并的子结构的悬垂。我们计算这些簇中相对于径向方向的平均场取向。在冷核簇的中央区域中,磁场的平均方向优先是方位角。使用X射线和无线电数据预测的Perseus的磁场之间存在广泛的一致性。进一步的数值研究和更高分辨率和更大有效领域的更好的未来观察将有助于减少这种方法的不确定性。
Magnetic fields in the intracluster medium (ICM) affect the structure and the evolution of galaxy clusters. However, their properties are largely unknown, and measuring magnetic fields in galaxy clusters is challenging, especially on large-scales outside of individual radio sources. In this work, we probe the plane-of-the-sky orientation of magnetic fields in clusters using the intensity gradients. The technique is a branch of the Gradient Technique (GT) that employs emission intensity maps from turbulent gas. We utilize the Chandra X-ray images of the Perseus, M 87, Coma, and A2597 galaxy clusters, and the VLA radio observations of the synchrotron emission from Perseus. We find that the fields predominantly follow the sloshing arms in Perseus, which is in agreement with numerical simulations. The GT-predicted magnetic field shows signatures of magnetic draping around rising bubbles driven by supermassive black hole (SMBH) feedback in the centers of cool-core clusters, as well as draping around substructures merging with the Coma cluster. We calculate the mean-field orientation with respect to the radial direction in these clusters. In the central regions of cool-core clusters, the mean orientation of the magnetic fields is preferentially azimuthal. There is a broad agreement between the magnetic field of Perseus predicted using the X-ray and radio data. Further numerical studies and better future observations with higher resolution and the larger effective area will help reduce the uncertainties of this method.