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arXiv 1712.04467astro-ph.GAastro-ph.CO
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用机器学习模拟重子对晕下粒子群的影响

Modeling the Impact of Baryons on Subhalo Populations with Machine Learning

Ethan O. Nadler, Yao-Yuan Mao, Risa H. Wechsler, Shea Garrison-Kimmel, Andrew Wetzel

摘要:

我们确定subhalos在暗物质-只有(DMO)放大模拟,很可能会被打乱,由于重子效应通过使用随机森林分类器训练的两个流体动力学模拟银河系(MW)质量主机晕从Latte套件的反馈在现实环境(FIRE)项目。我们训练我们的分类器,使用五个属性的每个中断和幸存的subhalo:在第一次近心通道吸积后的近心距离和比例因子,和比例因子,维里质量,吸积时的最大圆周速度。我们的五属性分类识别中断subhalos在消防模拟与$85\%$的袋外分类评分。我们预测幸存的subhalo人口在DMO模拟的消防主机晕,发现流体动力学的结果非常一致,特别是,我们的分类优于DMO放大模拟,包括在每个流体动力学模拟的中央星系盘的引力势,这表明它捕获的动力学效应的中央盘和额外的重子物理。我们还预测幸存的subhalo人口的一套DMO放大模拟MW质量主机晕,发现重子影响每个系统一致,预测的subhalo中断量大于主机到主机之间的分散subhalo人口。虽然我们的训练集的小尺寸和特定的重子物理处方限制了我们结果的一般性,但我们的工作表明,在流体动力学放大模拟上训练的机器学习分类算法可以有效地预测现实的subhalo种群。

英文摘要:

We identify subhalos in dark matter-only (DMO) zoom-in simulations that are likely to be disrupted due to baryonic effects by using a random forest classifier trained on two hydrodynamic simulations of Milky Way (MW)-mass host halos from the Latte suite of the Feedback in Realistic Environments (FIRE) project. We train our classifier using five properties of each disrupted and surviving subhalo: pericentric distance and scale factor at first pericentric passage after accretion, and scale factor, virial mass, and maximum circular velocity at accretion. Our five-property classifier identifies disrupted subhalos in the FIRE simulations with an $85\%$ out-of-bag classification score. We predict surviving subhalo populations in DMO simulations of the FIRE host halos, finding excellent agreement with the hydrodynamic results; in particular, our classifier outperforms DMO zoom-in simulations that include the gravitational potential of the central galactic disk in each hydrodynamic simulation, indicating that it captures both the dynamical effects of a central disk and additional baryonic physics. We also predict surviving subhalo populations for a suite of DMO zoom-in simulations of MW-mass host halos, finding that baryons impact each system consistently and that the predicted amount of subhalo disruption is larger than the host-to-host scatter among the subhalo populations. Although the small size and specific baryonic physics prescription of our training set limits the generality of our results, our work suggests that machine-learning classification algorithms trained on hydrodynamic zoom-in simulations can efficiently predict realistic subhalo populations.

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