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arXiv 2409.01181astro-ph.GA
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ISMGCC:利用高斯分解与图论在分子星际介质中寻找气体结构

ISMGCC: Finding Gas Structures in Molecular Interstellar Medium Using Gaussian Decomposition and Graph Theory

Haoran Feng, Zhiwei Chen, Zhibo Jiang, James S. Urquhart

AI总结:

本文提出ISMGCC方法,结合高斯分解与图论解决分子谱线数据中的过度连接问题,在MWISP数据中成功识别三百个分子气体结构并保留92%总流量。

摘要:

分子谱线发射通常用于示踪分子星际介质(ISM)的分布和性质。然而,由于相对较大的谱线宽度和旋臂的速度重叠,朝向内银河系的银道面上的发射严重混叠。基于PPV数据立方体中体素连通性的结构识别方法常常产生不切实际的大结构,这就是“过度连接”问题。因此,在这些方向上识别分子云结构并非易事。我们提出了一种基于高斯分解和图论的新方法来解决过度连接问题,命名为ISMGCC(星际介质高斯分量聚类)。利用MWISP ${}^{13}\mathrm{CO}~(1-0)$数据,在$13.5^{\circ} \leq l \leq 14.5^{\circ}, |b| \leq 0.5^{\circ}$以及$-100\leq V_{\mathrm{LSR}} \leq +200~\mathrm{km~s^{-1}}$范围内,我们的方法识别出三百个至少包含16个像素的分子气体结构。这些结构包含了原始数据立方体中总流量的$92\\%$,并在其超过$93\\%$的像素上呈现单峰谱线轮廓。ISMGCC方法能够区分拥挤区域中的气体结构,并在不进行全局数据裁剪或对结构几何形状作假设的情况下保留大部分流量,同时允许复杂谱线轮廓包含多个高斯分量。

英文摘要:

Molecular line emissions are commonly used to trace the distribution and properties of molecular Interstellar Medium (ISM). However, the emissions are heavily blended on the Galactic disk toward the inner Galaxy because of the relatively large line widths and the velocity overlaps of spiral arms. Structure identification methods based on voxel connectivity in PPV data cubes often produce unrealistically large structures, which is the ``over-linking'' problem. Therefore, identifying molecular cloud structures in these directions is not trivial. We propose a new method based on Gaussian decomposition and graph theory to solve the over-linking problem, named ISMGCC (InterStellar Medium Gaussian Component Clustering). Using the MWISP ${}^{13}\mathrm{CO}~(1-0)$ data in the range of $13.5^{\circ} \leq l \leq 14.5^{\circ}, |b| \leq 0.5^{\circ}$, and $-100\leq V_{\mathrm{LSR}} \leq +200~\mathrm{km~s^{-1}}$, our method identified three hundred molecular gas structures with at least 16 pixels. These structures contain $92\%$ of the total flux in the raw data cube and show single-peaked line profiles on more than $93\%$ of their pixels. The ISMGCC method could distinguish gas structures in crowded regions and retain most of the flux without global data clipping or assumptions on the structure geometry, meanwhile, allowing multiple Gaussian components for complicated line profiles.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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