arXiv 2109.046652021-09-10astro-ph.GA

附近云层中的高精度恒星形成效率测量

High-precision star formation efficiency measurements in nearby clouds

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

备注：: 5 pages, 4 figures, submitted to MNRAS Letter
DOI：: 10.1093/mnras/stac174

摘要：

平均而言，分子云在每一次自由落体时间内只将其质量的一小部分转化为恒星，但不同的恒星形成理论对这种低平均效率是如何实现的提出了截然不同的主张。为了验证这些理论，我们需要精确测量平均值和散射的平均值，但高精度的测量一直很困难，因为它们需要确定云的体积密度，我们可以从中计算自由落体时间。直到最近，大多数密度估计都假设云是均匀的球体，而它们的真实结构通常是不规则的和高度不均匀的，从而在云密度估计中产生系统误差，并模糊了真实的云与云之间的变化。我们最近开发了一种理论模型，通过使用云中的柱密度分布来减少这种误差，以产生更准确的体积密度估计。在这封信中，我们将这个模型应用到最近对附近12个分子云的观测中。与早期的分析相比，我们的方法减少了典型的分散体内的个别云从0.35 dex到0.31 dex，并降低了所有云的平均值从~ 0.02到~ 0.01。然而，我们没有发现在~ 0.2 dex的云到云的色散的dex_ff的显着变化，这表明所测量的色散反映了真正的云之间的结构差异。

英文摘要：

On average molecular clouds convert only a small fraction epsilon_ff of their mass into stars per free-fall time, but differing star formation theories make contrasting claims for how this low mean efficiency is achieved. To test these theories, we need precise measurements of both the mean value and the scatter of epsilon_ff, but high-precision measurements have been difficult because they require determining cloud volume densities, from which we can calculate free-fall times. Until recently, most density estimates assume clouds as uniform spheres, while their real structures are often filamentary and highly non-uniform, yielding systematic errors in epsilon_ff estimates and smearing real cloud-to-cloud variations. We recently developed a theoretical model to reduce this error by using column density distributions in clouds to produce more accurate volume density estimates. In this letter, we apply this model to recent observations of 12 nearby molecular clouds. Compared to earlier analyses, our method reduces the typical dispersion of epsilon_ff within individual clouds from 0.35 dex to 0.31 dex, and decreases the median value of epsilon_ff over all clouds from ~ 0.02 to ~ 0.01. However, we find no significant change in the ~ 0.2 dex cloud-to-cloud dispersion of epsilon_ff, suggesting the measured dispersions reflect real structural differences between clouds.

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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