论文标题
Simba模拟中的Baryonic Tully-Fisher关系
The baryonic Tully-Fisher relation in the Simba simulation
论文作者
论文摘要
我们研究了$（100 \，h^{ - 1} {\ rm mpc}）^3 $ SIMBA流体动力学星系仿真模拟$（100 \，h^{ - 1} { - 1} { - 1} {\ rm mpc}）中的Baryonic tully-fisher关系（BTFR），以及高分辨率$（25 \，H^{ - 1} { - 1} { - 1} {\ rm mpc $ sim $ sims^3 $ ni，星系。我们从质量分布中产生模拟的星系旋转曲线，我们显示的结果与使用气体旋转速度的结果相似。由此，我们使用四个指标来测量Galaxy旋转速度$ v _ {\ rm Circ} $：$ v _ {\ rm max}，v _ {\ rm flat}，v_ {2r_e}，$ v_ {2r_e}，$ v _ {\ rm polyex} $。我们将预测的BTFR与SPARC观察样本进行了比较，并找到了广泛的一致性。但是，详细说明，Simba偏向更高的$ v _ {\ rm Circ} $，最高为0.1 DEX。我们发现证据表明BTFR在$ v _ {\ rm Circ}> 300 $ km s $^{ - 1} $ galaxies中与最近的观察结果一致。与观察结果相比，Simba的旋转曲线对于较低的质量星系的峰值更高，这表明我们的样品中有过度凸起的矮小星系。我们研究了BTFR与HI质量，恒星质量，气体分数和特定恒星形成率周围的残留物，这些速率为即将进行的BTFR调查提供了可测试的预测。 Simba的BTFR显示了次优的分辨率融合，高分辨率运行降低了与数据的更好一致的$ V $。
We investigate the Baryonic Tully-Fisher Relation (BTFR) in the $(100\,h^{-1}{\rm Mpc})^3$ Simba hydrodynamical galaxy formation simulation together with a higher-resolution $(25\,h^{-1}{\rm Mpc})^3$ Simba run, for over $10,000$ disk-dominated, HI-rich galaxies. We generate simulated galaxy rotation curves from the mass distribution, which we show yields similar results to using the gas rotational velocities. From this we measure the galaxy rotation velocity $V_{\rm circ}$ using four metrics: $V_{\rm max}, V_{\rm flat}, V_{2R_e},$ and $V_{\rm polyex}$. We compare the predicted BTFR to the SPARC observational sample and find broad agreement. In detail, however, Simba is biased towards higher $V_{\rm circ}$ by up to 0.1 dex. We find evidence for the flattening of the BTFR in $V_{\rm circ}>300$ km s$^{-1}$ galaxies, in agreement with recent observational findings. Simba's rotation curves are more peaked for lower mass galaxies, in contrast with observations, suggesting overly bulge-dominated dwarf galaxies in our sample. We investigate for residuals around the BTFR versus HI mass, stellar mass, gas fraction, and specific star formation rate, which provide testable predictions for upcoming BTFR surveys. Simba's BTFR shows sub-optimal resolution convergence, with the higher-resolution run lowering $V$ in better agreement with data.
