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arXiv 1707.01427astro-ph.GA
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再电离过程中晕重子分数的人工神经网络约束

Using Artificial Neural Networks to Constrain the Halo Baryon Fraction during Reionization

David Sullivan, Ilian T. Iliev, Keri L. Dixon

摘要:

来自恒星和星系的辐射反馈被认为是解决基于$Λ$ CDM的简单星系形成模型中许多矛盾的一种可能方法,例如紫外光度函数的暗端。辐射的总能量预算可能超过银河风和超新星的总和,这推动了复杂算法的发展,这些算法在宇宙学背景下同时发展辐射场和气体的流体动力学响应。我们探测星系尺度上的自反馈使用自适应网格细化,辐射转移,流体力学,和$N$体代码。与以往的研究,假设一个均匀的紫外背景,我们自洽地发展的辐射场和气体约束的晕重子分数在宇宙再电离。我们表明,平均重子分数的一半的宇宙平均值,$M_{\mathrm{c}}(z)$的特征晕质量,表现出很小的变化作为质量加权电离分数的函数。此外,我们发现,包括金属冷却和解决尺度足够小的自屏蔽成为有效的能力,导致$M_{\mathrm{c}}$显着下降时,最近的研究相比。最后,我们开发了一个人工神经网络,能够预测的重子分数的晕最近的潮汐相互作用,气体温度和质量加权电离分数的基础上。这样的模型可以应用于任何再电离的历史,并平凡地纳入半解析模型的星系形成。

英文摘要:

Radiative feedback from stars and galaxies has been proposed as a potential solution to many of the tensions with simplistic galaxy formation models based on $Λ$CDM, such as the faint end of the UV luminosity function. The total energy budget of radiation could exceed that of galactic winds and supernovae combined, which has driven the development of sophisticated algorithms that evolve both the radiation field and the hydrodynamical response of gas simultaneously, in a cosmological context. We probe self-feedback on galactic scales using the adaptive mesh refinement, radiative transfer, hydrodynamics, and $N$-body code. Unlike previous studies which assume a homogeneous UV background, we self-consistently evolve both the radiation field and gas to constrain the halo baryon fraction during cosmic reionization. We demonstrate that the characteristic halo mass with mean baryon fraction half the cosmic mean, $M_{\mathrm{c}}(z)$, shows very little variation as a function of mass-weighted ionization fraction. Furthermore, we find that the inclusion of metal cooling and the ability to resolve scales small enough for self-shielding to become efficient leads to a significant drop in $M_{\mathrm{c}}$ when compared to recent studies. Finally, we develop an Artificial Neural Network that is capable of predicting the baryon fraction of haloes based on recent tidal interactions, gas temperature, and mass-weighted ionization fraction. Such a model can be applied to any reionization history, and trivially incorporated into semi-analytical models of galaxy formation.

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