Revisiting HOD model assumptions: the impact of AGN feedback and assembly bias
Revisiting HOD model assumptions: the impact of AGN feedback and assembly bias
复制标题
重新审视 HOD 模型假设：AGN 反馈和装配偏差的影响
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Helly
中科院分区：
文献类型：
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作者：
N. McCullagh;P. Norberg;S. Cole;V. Gonzalez;C. Baugh;J. Helly
标准的Halo Occupation Distribution（HOD）模型最初是基于半解析和流体动力学星系形成模型的结果开发的。这些模型已经取得了进展，特别是包括AGN反馈，以匹配宇宙中的星系光度函数与观测到的重子分数。活动星系核反馈影响星系恒星质量和光度之间的关系，特别是使关系非单调。对于匹配的数密度样本，星系的亮度阈值样本中占据了不同的范围内的晕质量的恒星质量阈值样本。我们发现，在半解析星系形成模型，包括AGN的反馈比标准HOD模型假设的光度阈值样本的HOD的形状稍微复杂。我们还发现，subhalo丰度匹配（SHAM）不保留这些非标准的形状。我们发现，使用SHAM和半解析模型Galform创建的目录，具有相同的大规模2点聚类的建设有不同的空概率函数（VPF）在真实的和红移空间。我们发现，这些差异产生于不同的HOD形状，而不是组装偏差，这表明VPF可以用来测试的适用性HOD模型与真实的数据。
The standard Halo Occupation Distribution (HOD) models were originally developed based on results from semi-analytic and hydrodynamical galaxy formation models. Those models have since progressed, in particular to include AGN feedback to match the galaxy luminosity function in a universe with the observed baryon fraction. AGN feedback affects the relationship between galaxy stellar mass and luminosity, in particular making the relationship non-monotonic. For matched number density samples, galaxies in luminosity-threshold samples occupy a different range of halo masses from those in stellar-mass-threshold samples. We find that the shapes of the HODs of luminosity-threshold samples are slightly more complicated in semi-analytic galaxy formation models that include AGN feedback than are assumed by standard HOD models. We also find that subhalo abundance matching (SHAM) does not preserve these non-standard shapes. We show that catalogues created using SHAM and the semi-analytic model Galform that have the same large-scale 2-point clustering by construction have different void probability functions (VPFs) in both real and redshift space. We find that these differences arise from the different HOD shapes, as opposed to assembly bias, which indicates that the VPF could be used to test the suitability of an HOD model with real data.