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arXiv 1712.03255astro-ph.GA
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使用机器学习将星系绘制成暗物质晕

Painting galaxies into dark matter halos using machine learning

Shankar Agarwal, Romeel Davé, Bruce A. Bassett

备注：
15 pages, 10 figures, 1 table, accepted version from MNRAS
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摘要：

我们开发了一个机器学习（ML）框架来填充大型暗物质-只有重子星系的模拟。我们的ML框架输入晕属性，包括晕质量，环境，自旋和最近的增长历史，并输出中心星系和晕重子属性，包括恒星质量（$M_*$），恒星形成率（SFR），金属丰度（$Z$），中性（$\rm HI$）和分子（$\rm H_2$）氢质量。我们将其应用于MUFASA宇宙流体动力学模拟，并表明它恢复了输出量与晕质量的平均趋势高度准确，包括在淬火的大质量星系中SFR和气体的急剧下降。然而，平均值关系周围的离散度预测不足。单独检查星系，在$z=0$上，恒星质量和金属丰度被准确地恢复（$σ\lesssim 0.2$ ~dex），但SFR和$\rm HI$显示出更大的散射（$σ\gtrsim 0.3$ ~dex）;这些值在$z=1,2$上有所改善。值得注意的是，ML定量恢复星系属性的第二个参数的趋势，例如，具有较高的气体含量和较低的金属丰度的星系有较高的SFR在一个给定的$M_*$。测试各种ML算法，我们发现没有一种算法的性能明显优于其他算法，集成也没有提高性能，这可能是因为没有一种算法重现了在平均值属性周围观察到的大分散。对于随机森林算法，我们发现晕质量和附近的（$\sim 200$ ~kpc）环境是最重要的预测变量，其次是生长历史，而晕自旋和$\sim$ Mpc尺度环境是不重要的。最后，我们研究的影响，另外输入的关键重子属性$M_*$，SFR和$Z$，将是例如从平衡模型，并表明，特别是提供SFR使$\rm HI$被恢复得更准确。

英文摘要：

We develop a machine learning (ML) framework to populate large dark matter-only simulations with baryonic galaxies. Our ML framework takes input halo properties including halo mass, environment, spin, and recent growth history, and outputs central galaxy and halo baryonic properties including stellar mass ($M_*$), star formation rate (SFR), metallicity ($Z$), neutral ($\rm HI$) and molecular ($\rm H_2$) hydrogen mass. We apply this to the MUFASA cosmological hydrodynamic simulation, and show that it recovers the mean trends of output quantities with halo mass highly accurately, including following the sharp drop in SFR and gas in quenched massive galaxies. However, the scatter around the mean relations is under-predicted. Examining galaxies individually, at $z=0$ the stellar mass and metallicity are accurately recovered ($σ\lesssim 0.2$~dex), but SFR and $\rm HI$ show larger scatter ($σ\gtrsim 0.3$~dex); these values improve somewhat at $z=1,2$. Remarkably, ML quantitatively recovers second parameter trends in galaxy properties, e.g. that galaxies with higher gas content and lower metallicity have higher SFR at a given $M_*$. Testing various ML algorithms, we find that none perform significantly better than the others, nor does ensembling improve performance, likely because none of the algorithms reproduce the large observed scatter around the mean properties. For the random forest algorithm, we find that halo mass and nearby ($\sim 200$~kpc) environment are the most important predictive variables followed by growth history, while halo spin and $\sim$Mpc scale environment are not important. Finally we study the impact of additionally inputting key baryonic properties $M_*$, SFR and $Z$, as would be available e.g. from an equilibrium model, and show that particularly providing the SFR enables $\rm HI$ to be recovered substantially more accurately.

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