MAGNETICALLY ALIGNED HI FIBERS AND THE ROLLING HOUGH TRANSFORM
MAGNETICALLY ALIGNED HI FIBERS AND THE ROLLING HOUGH TRANSFORM
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DOI：
10.1088/0004-637x/789/1/82
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发表时间：
2014-07-01
影响因子：
4.9
通讯作者：
Putman, M. E.
中科院分区：
文献类型：
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作者：
Clark, S. E.;Peek, J. E. G.;Putman, M. E.
我们展示了对漫射银河系星际介质（ISM）中一组新结构的观测结果：细长的线性Hi特征，我们称之为“纤维”，在银河系高纬度延伸了许多度。为了表征和测量这些纤维的程度和强度，我们提出了滚动霍夫变换，这是一种新的机器视觉方法，用于参数化图像平面上结构的相干线性度。有了这个强大的新工具，我们发现光纤是沿着星际磁场取向的，这是由星光偏振探测到的。我们发现这些低柱密度(N-HI相似或等于5 x 10（18) cm(-2)）的纤维特征很可能是大约100 pc外的局部腔壁的组成部分。我们用来证明这种高纬度排列的Hi数据来自银河阿雷西博l波段馈电阵列Hi （GALFA-H I）巡天和帕克斯银河全天巡天。我们在高分辨率的GALFA-H - I数据中发现了更好的对准，其中的纤维在视觉上更加明显。这一趋势在我们对Riegel-Crutcher H I冷云中磁性排列的线性特征的研究中继续存在，这是在南银河面调查中发现的。我们提出了一种基于钱德拉塞卡-费米方法的RHT用于估计这种云中的场强的应用。我们得出的结论是，数据驱动的ISM形态学定量研究可以非常有力地预测潜在的物理量。
We present observations of a new group of structures in the diffuse Galactic interstellar medium (ISM): slender, linear Hi features we dub "fibers" that extend for many degrees at high Galactic latitude. To characterize and measure the extent and strength of these fibers, we present the Rolling Hough Transform, a new machine vision method for parameterizing the coherent linearity of structures in the image plane. With this powerful new tool we show that the fibers are oriented along the interstellar magnetic field as probed by starlight polarization. We find that these low column density (N-HI similar or equal to 5 x 10(18) cm(-2)) fiber features are most likely a component of the local cavity wall, about 100 pc away. The Hi data we use to demonstrate this alignment at high latitude are from the Galactic Arecibo L-Band Feed Array Hi (GALFA-H I) Survey and the Parkes Galactic All Sky Survey. We find better alignment in the higher resolution GALFA-H I data, where the fibers are more visually evident. This trend continues in our investigation of magnetically aligned linear features in the Riegel-Crutcher H I cold cloud, detected in the Southern Galactic Plane Survey. We propose an application of the RHT for estimating the field strength in such a cloud, based on the Chandrasekhar-Fermi method. We conclude that data-driven, quantitative studies of ISM morphology can be very powerful predictors of underlying physical quantities.