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Algorithm for extracting intersections in PPV space of filaments
Research in Astronomy and Astrophysics ( IF 1.8 ) Pub Date : 2021-03-19 , DOI: 10.1088/1674-4527/21/2/48
Chao Zhang 1, 2 , Zhi-Yuan Ren 1 , Chen Wang 3 , Jing-Wen Wu 1 , Xiao-Yun Ma 1
Affiliation  

A filament is an important structure for studying star formation, especially intersections of filaments which are believed to be more dense than other regions. Identifying filament intersections is the first step in studying them. Current methods can only extract two-dimensional intersections without considering the velocity dimension. In this paper, we propose a method to identify three-dimensional (3D) intersections by combining Harris Corner Detection and Hough Line Transform, which achieve a precision of 98%. We apply this method for extracting intersection structures of the OMC-2/3 molecular cloud and to study its physical properties and obtain the associated PDF distribution. Results show denser gas is concentrated in those 3D intersections.



中文翻译:

细丝PPV空间交叉点提取算法

细丝是研究恒星形成的重要结构,尤其是被认为比其他区域更密集的细丝交叉点。识别细丝交叉点是研究它们的第一步。目前的方法只能提取二维交点而不考虑速度维度。在本文中,我们提出了一种结合哈里斯角检测和霍夫线变换来识别三维(3D)交叉点的方法,其精度达到 98%。我们应用这种方法来提取 OMC-2/3 分子云的交叉结构,并研究其物理性质并获得相关的 PDF 分布。结果显示,较稠密的气体集中在这些 3D 交叉点。

更新日期:2021-03-19
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