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arXiv 0812.1183astro-ph
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模拟z=2时的恒星形成宇宙:冷吸积流的影响

Modeling The Star Forming Universe at z=2: Impact of Cold Accretion Flows

S. Khochfar, J. Silk

摘要:

我们提出了一个半解析模型(SAM),其中包括冷吸积和基于孔隙度的处方恒星形成的结果。如果我们允许来自冷流的18%的吸积能量驱动$z=2$的气体盘中的湍流,我们可以恢复在各种大质量盘或盘状星系中看到的低$V/σ$的令人困惑的观测结果。通过冷流的气体质量的增加本身是不足以增加恒星形成率足以恢复$\dot{M}_*>120$ M $_{\odot}$ yr $^{-1}$星系在我们的模型中的数密度。此外,还需要提高恒星形成效率。这可以在孔隙度模型中自然地实现,其中恒星形成效率与$\propto σ$成比例,而$\propto σ$与云速度色散成比例。由于冷吸积是我们模型中气体速度分散的主要驱动力,因此恒星形成效率与冷吸积速率平行,并允许快速转换成恒星。在$z\sim 2$,我们发现一个空间密度$~10^{-4}$ Mpc $^{-3}$在恒星形成星系与$\dot{M}_*>120$ M $_{\odot}$ yr $^{-1}$,在更好的协议比以前的估计形式SAM。然而,$\dot{M}_*$和$M_*$之间的基本关系仍然偏离了观测到的关系,这表明在高Z值可能需要更有效的恒星形成,这可能与活动星系核触发的作用有关。

英文摘要:

We present results of a semi-analytic model (SAM) that includes cold accretion and a porosity-based prescription for star formation. We can recover the puzzling observational results of low $V/σ$ seen in various massive disk or disk-like galaxies, if we allow 18 % of the accretion energy from cold flows to drive turbulence in gaseous disks at $z=2$. The increase of gas mass through cold flows is by itself not sufficient to increase the star formation rate sufficiently to recover the number density of $\dot{M}_*>120$ M$_{\odot}$ yr$^{-1}$ galaxies in our model. In addition, it is necessary to increase the star formation efficiency. This can be achieved naturally in the porosity model, where star formation efficiency scales $\propto σ$, which scales as cloud velocity dispersion. As cold accretion is the main driver for gas velocity dispersion in our model, star formation efficiency parallels cold accretion rates, and allows fast conversion into stars. At $z\sim 2$, we find a space density $~10^{-4}$ Mpc$^{-3}$ in star-forming galaxies with $\dot{M}_*>120$ M$_{\odot}$ yr$^{-1}$, in better agreement than earlier estimates form SAMs. However, the fundamental relation between $\dot{M}_*$ and $M_*$ is still offset from the observed relation, indicating the need for possibly more efficient star formation at high z perhaps associated with a role for AGN triggering.

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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