论文标题
在模拟的银河系形成中测试来自星团的反馈
Testing Feedback from Star Clusters in Simulations of the Milky Way Formation
论文作者
论文摘要
我们提出了一套星系构造模拟,该模拟直接模拟了星形簇的形成和破坏。从我们小组先前开发的模型开始,在这里我们介绍了群集形成和反馈的处方的几个改进,然后使用大量的银河系大量星系的宇宙学模拟来测试这些更新。我们执行差异分析,目的是了解每个更新如何影响星团群体。两个关键参数是超新星反馈的动量提升$ f _ {\ mathrm {boost}} $和每次自由落体时间$ε_{\ mathrm {ff}} $的星形形成效率。我们发现$ f _ {\ mathrm {boost}} $对银河星形成率有很大的影响,较高的值导致恒星形成较少。效率$ε_ {\ mathrm {ff}} $对全球恒星形成率没有重大影响,但会大大改变群集的特性,随着$ε_ {\ mathrm {ff}} $的增加,导致更高的最大群集质量,较小的星星在Clusters和集成的集成级别中,并成立了更高的星星。我们还探索了可观察到的群集质量功能的红移演变,发现最大的群集在高红移$ z> 4 $上形成。群集破坏到$ z = 0 $的外推与银河球体群集质量函数和年龄金属关系都产生了良好的一致性。我们的结果强调了使用星系的小规模特性来校准星团形成和反馈的亚电网模型的重要性。
We present a suite of galaxy formation simulations that directly model star cluster formation and disruption. Starting from a model previously developed by our group, here we introduce several improvements to the prescriptions for cluster formation and feedback, then test these updates using a large suite of cosmological simulations of Milky Way mass galaxies. We perform a differential analysis with the goal of understanding how each of the updates affects star cluster populations. Two key parameters are the momentum boost of supernova feedback $f_{\mathrm{boost}}$ and star formation efficiency per freefall time $ε_{\mathrm{ff}}$. We find that $f_{\mathrm{boost}}$ has a strong influence on the galactic star formation rate, with higher values leading to less star formation. The efficiency $ε_{\mathrm{ff}}$ does not have a significant impact on the global star formation rate, but dramatically changes cluster properties, with increasing $ε_{\mathrm{ff}}$ leading to a higher maximum cluster mass, shorter age spread of stars within clusters, and higher integrated star formation efficiencies. We also explore the redshift evolution of the observable cluster mass function, finding that most massive clusters have formed at high redshift $z>4$. Extrapolation of cluster disruption to $z=0$ produces good agreement with both the Galactic globular cluster mass function and age-metallicity relation. Our results emphasize the importance of using small-scale properties of galaxies to calibrate subgrid models of star cluster formation and feedback.