What is a GMC? Are observers and simulators discussing the same star-forming clouds?
What is a GMC? Are observers and simulators discussing the same star-forming clouds?
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什么是GMC？
DOI：
10.1093/mnras/stv1843
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发表时间：
2015
影响因子：
4.8
通讯作者：
James
中科院分区：
文献类型：
--
作者：
Pan;Hsi-An; Fujimoto;Yusuke; Tasker;Elizabeth J.; Rosolowsky;Erik; Colombo;Dario; Benincasa;Samantha M.; Wadsley;James
随着模拟和观测都达到了恒星形成分子云的分辨率，澄清这两种技术是否在讨论星系中的同一物体变得很重要。我们比较了在高分辨率星系模拟中形成的云，这些云被识别为等高线内的连续结构，在模拟器的位置-位置-位置(PPP)坐标空间和观测者的位置-位置-速度空间(PPV)中。结果表明，在两种方法中，云群体的性质是相似的，并且高达70 / 的云在相反的数据结构中具有单一的对应物。在一对一匹配的情况下，比较各个云的性质，发现它们的散布大多在2倍以内。然而，质量、半径和速度色散的微小变化会导致导出量的显著差异，如维里参数。这使得很难确定一个结构是否真的受到引力的约束。Fujimoto等人最初在模拟中发现的三种云类型。在两个数据集中都被识别，大约80 / %的云在识别方法之间保留了它们的类型。我们还比较了使用峰值分解方法识别PPP和PPV空间中的云时的结果。使用该技术，云的数量增加了，但总体云属性保持不变。然而，更拥挤的环境将不同技术之间匹配云的能力降低到40 / Cent。这三种云类型也变得更难分离，特别是在PPV数据集中。因此，用于云识别的方法在确定云的属性方面起着关键作用，但PPP和PPV都有可能识别相同的结构。
As both simulations and observations reach the resolution of the star-forming molecular clouds, it becomes important to clarify if these two techniques are discussing the same objects in galaxies. We compare clouds formed in a high-resolution galaxy simulation identified as continuous structures within a contour, in the simulator's position–position–position (PPP) coordinate space and the observer's position–position–velocity space (PPV). Results indicate that the properties of the cloud populations are similar in both methods and up to 70 per cent of clouds have a single counterpart in the opposite data structure. Comparing individual clouds in a one-to-one match reveals a scatter in properties mostly within a factor of 2. However, the small variations in mass, radius and velocity dispersion produce significant differences in derived quantities such as the virial parameter. This makes it difficult to determine if a structure is truly gravitationally bound. The three cloud types originally found in the simulation in Fujimoto et al. are identified in both data sets, with around 80 per cent of the clouds retaining their type between identification methods. We also compared our results when using a peak decomposition method to identify clouds in both PPP and PPV space. The number of clouds increased with this technique, but the overall cloud properties remained similar. However, the more crowded environment lowered the ability to match clouds between techniques to 40 per cent. The three cloud types also became harder to separate, especially in the PPV data set. The method used for cloud identification therefore plays a critical role in determining cloud properties, but both PPP and PPV can potentially identify the same structures.