arXiv 2011.107882020-11astro-ph.GAastro-ph.IM

从分子气体的二维观测重建三维密度

Reconstructing three-dimensional densities from two-dimensional observations of molecular gas

Zipeng Hu, Mark R. Krumholz, Christoph Federrath, Riwaj Pokhrel, Robert A. Gutermuth

备注：: 14 pages, 14 figures. Accepted by MNRAS
DOI：: 10.1093/mnras/stab356

摘要：

长期以来，恒星的形成一直被认为是一个效率低下的过程，在这个意义上，任何给定的气体云的质量中，只有一小部分$ε_{\rm ff}$在每个云的自由落体时间内转化为恒星。然而，开发成功的恒星形成理论需要测量$ε_{\rm ff}$的平均值及其从一个分子云到另一个分子云的散射。因为$ε_{\rm ff}$是相对于自由落体时间测量的，所以这种测量需要精确地确定云的体积密度。然而，从二维投影数据测量体积密度的努力迄今为止一直依赖于将分子云视为简单的均匀球体，而它们的真实形状可能是不规则的，并且它们的密度分布远非均匀。由此产生的真实体积密度的不确定性可能是$ε_{\rm ff}$观测估计误差的主要来源之一。在本文中，我们使用了一套模拟湍流，磁化，辐射，自引力恒星形成的云，以检查是否有可能获得更准确的体积密度估计，从而减少这种错误。我们从模拟中创建模拟观测，并表明目前的分析方法依赖于球形假设可能会产生约0.26 dex低估和约0.51 dex误差的体积密度估计，对应于约0.13 dex高估和约0.25 dex分散在$ε_{\rm ff}$，在观测到的云样本的分散。我们建立了一个预测模型，该模型使用二维测量中可获得的信息-最显着的是表面密度分布的基尼系数-以估计体积密度，其中约0.3 dex较少散射。我们测试我们的方法在最近的观测蛇夫座云，并表明，它成功地减少了$ε_{\rm ff}$散射。

英文摘要：

Star formation has long been known to be an inefficient process, in the sense that only a small fraction $ε_{\rm ff}$ of the mass of any given gas cloud is converted to stars per cloud free-fall time. However, developing a successful theory of star formation will require measurements of both the mean value of $ε_{\rm ff}$ and its scatter from one molecular cloud to another. Because $ε_{\rm ff}$ is measured relative to the free-fall time, such measurements require accurate determinations of cloud volume densities. Efforts to measure the volume density from two-dimensional projected data, however, have thus far relied on treating molecular clouds as simple uniform spheres, while their real shapes are likely filamentary and their density distributions far from uniform. The resulting uncertainty in the true volume density is likely one of the major sources of error in observational estimates of $ε_{\rm ff}$. In this paper, we use a suite of simulations of turbulent, magnetized, radiative, self-gravitating star-forming clouds to examine whether it is possible to obtain more accurate volume density estimates and thereby reduce this error. We create mock observations from simulations, and show that current analysis methods relying on the spherical assumption likely yield ~ 0.26 dex underestimations and ~ 0.51 dex errors in volume density estimates, corresponding to a ~ 0.13 dex overestimation and a ~ 0.25 dex scatter in $ε_{\rm ff}$, comparable to the scatter in observed cloud samples. We build a predictive model that uses information accessible in two-dimensional measurements -- most significantly the Gini coefficient of the surface density distribution -- to estimate volume density with ~ 0.3 dex less scatter. We test our method on a recent observation of the Ophiuchus cloud, and show that it successfully reduces the $ε_{\rm ff}$ scatter.

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