论文标题
Cloud Factory II:在银河电位中分辨分子云的重视运动学运动学
The Cloud Factory II: Gravoturbulent Kinematics of Resolved Molecular Clouds in a Galactic Potential
论文作者
论文摘要
我们介绍了从我们的“云工厂”的银河系尺度ISM模拟套件中提取的分子云复合物中的引诱速度波动的统计分析。为此,我们生产非LTE $^{12} $ co j = 1-0合成观测值,并将主要成分分析(PCA)还原技术应用于云配合物的代表性样本。速度波动是在我们的模拟中发挥作用的不同物理机制自我一致的,其中包括银河级力量,气体自我重力和超新星反馈。统计分析表明,即使纯粹的重力效应对于复制标准观察定律是必要的,在大多数情况下它们也不足够。我们表明,超新星爆炸的额外注入能量在建立全球湍流场以及分子云的局部动力学和形态方面起着关键作用。此外,我们表征了由于云环境条件的结果表征结构函数缩放参数:某些复合物浸入了弥漫性(臂间)或致密(螺旋 - 臂)环境中,而其他复合物则受嵌入式或外部超新星的影响。在静止区域,我们获得了由重力塌陷和超音速湍流驱动的缩放参数的随时间不断发展的轨迹。我们的发现表明,一项基于PCA的统计研究是诊断出驱动分子云的重视特性的物理机制的强大方法。此外,我们提出了一个新的开源模块,即PCAFACTORY,该模块巧妙地执行PCA以以用户友好的方式从ISM的模拟或真实数据中提取速度结构函数。软件doi:10.5281/Zenodo.3822718
We present a statistical analysis of the gravoturbulent velocity fluctuations in molecular cloud complexes extracted from our "Cloud Factory" galactic-scale ISM simulation suite. For this purpose, we produce non-LTE $^{12}$CO J=1-0 synthetic observations and apply the Principal Component Analysis (PCA) reduction technique on a representative sample of cloud complexes. The velocity fluctuations are self-consistently generated by different physical mechanisms at play in our simulations, which include galactic-scale forces, gas self-gravity, and supernova feedback. The statistical analysis suggests that, even though purely gravitational effects are necessary to reproduce standard observational laws, they are not sufficient in most cases. We show that the extra injection of energy from supernova explosions plays a key role in establishing the global turbulent field and the local dynamics and morphology of molecular clouds. Additionally, we characterise structure function scaling parameters as a result of cloud environmental conditions: some of the complexes are immersed in diffuse (inter-arm) or dense (spiral-arm) environments, and others are influenced by embedded or external supernovae. In quiescent regions, we obtain time-evolving trajectories of scaling parameters driven by gravitational collapse and supersonic turbulent flows. Our findings suggests that a PCA-based statistical study is a robust method to diagnose the physical mechanisms that drive the gravoturbulent properties of molecular clouds. Also, we present a new open source module, the PCAFACTORY, which smartly performs PCA to extract velocity structure functions from simulated or real data of the ISM in a user-friendly way. Software DOI: 10.5281/zenodo.3822718