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arXiv 2001.07214astro-ph.GA
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亚网格模型对星系形成模拟中巨分子云性质的影响

The effects of subgrid models on the properties of giant molecular clouds in galaxy formation simulations

Hui Li, Mark Vogelsberger, Federico Marinacci, Laura Sales, Paul Torrey

摘要:

最近的宇宙学流体动力学模拟能够再现与观测数据一致的星系的许多统计特性。然而,所采用的子网格模型强烈影响模拟结果,限制了这些模拟的预测能力。在这项工作中,我们进行了一套孤立的星系盘模拟下的{ \it SMUGGLE}框架和调查如何不同的亚网格模型影响巨大的分子云(GMC)的属性。我们采用{ \sc astrodendro},一个分层的聚类发现算法,以确定GMC的模拟。我们发现,不同的选择亚网格恒星形成效率,$ε_{\rm ff}$,和恒星反馈通道,产生显着不同的质量和空间分布的GMC人口。没有反馈,GMC的质量函数具有较浅的幂律斜率,并延伸到更高的质量范围相比,运行与反馈。此外,较高的$ε_{\rm ff}$导致更快的分子气体消耗和更陡的质量函数斜率。反馈还抑制GMC的空间分布的两点关联函数(TPCF)中的功率。具体来说,辐射反馈强烈降低TPCF的尺度低于0.2~kpc,而超新星反馈降低功率的尺度高于0.2~kpc。最后,具有较高的$ε_{\rm ff}$的运行比具有较低的$ε_{\rm ff}$的运行表现出更高的TPCF,因为致密气体被更有效地耗尽,从而促进结构良好的超新星气泡的形成。我们认为,比较模拟和观察到的GMC人口可以帮助更好地约束亚网格模型在下一代的星系形成模拟。

英文摘要:

Recent cosmological hydrodynamical simulations are able to reproduce numerous statistical properties of galaxies that are consistent with observational data. Yet, the adopted subgrid models strongly affect the simulation outcomes, limiting the predictive power of these simulations. In this work, we perform a suite of isolated galactic disk simulations under the {\it SMUGGLE} framework and investigate how different subgrid models affect the properties of giant molecular clouds (GMCs). We employ {\sc astrodendro}, a hierarchical clump-finding algorithm, to identify GMCs in the simulations. We find that different choices of subgrid star formation efficiency, $ε_{\rm ff}$, and stellar feedback channels, yield dramatically different mass and spatial distributions for the GMC populations. Without feedback, the mass function of GMCs has a shallower power-law slope and extends to higher mass ranges compared to runs with feedback. Moreover, higher $ε_{\rm ff}$ results in faster molecular gas consumption and steeper mass function slopes. Feedback also suppresses power in the two-point correlation function (TPCF) of the spatial distribution of GMCs. Specifically, radiative feedback strongly reduces the TPCF on scales below 0.2~kpc, while supernova feedback reduces power on scales above 0.2~kpc. Finally, runs with higher $ε_{\rm ff}$ exhibit a higher TPCF than runs with lower $ε_{\rm ff}$, because the dense gas is depleted more efficiently thereby facilitating the formation of well-structured supernova bubbles. We argue that comparing simulated and observed GMC populations can help better constrain subgrid models in the next-generation of galaxy formation simulations.

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