论文标题
暗物质光环浓度的随机步行模型
A Random Walk Model for Dark Matter Halo Concentrations
论文作者
论文摘要
对于理想化的（球形，光滑）的暗光晕，由单参数密度曲线（例如NFW曲线）描述，在光环的能量与其密度曲线的比例半径之间存在一对一的映射。因此，能量独特地确定了这种光晕的浓度参数。我们利用这一事实来通过在光晕能量空间中随机步行来预测暗物质光晕的浓度。鉴于对光环的完整合并树，该树中每个光环的总内能是通过求和祖细胞晕的内部和轨道能量来确定的。我们表明，校准后，该模型可以准确地重现在N体模拟中测量的浓度 - 质量关系的平均值，并比以前的模型相比，在该关系中重现更多的散布。我们通过检查跨时间的比例半径的自相关以及光晕浓度和自旋之间的相关性以及与宇宙N体仿真测得的结果相比，进一步测试了该模型。在这两种情况下，我们都发现我们的模型与N体结果紧密匹配。我们的模型是在开源Galacticus工具包中实现的。
For idealized (spherical, smooth) dark matter halos described by single-parameter density profiles (such as the NFW profile) there exists a one-to-one mapping between the energy of the halo and the scale radius of its density profile. The energy therefore uniquely determines the concentration parameter of such halos. We exploit this fact to predict the concentrations of dark matter halos via a random walk in halo energy space. Given a full merger tree for a halo, the total internal energy of each halo in that tree is determined by summing the internal and orbital energies of progenitor halos. We show that, when calibrated, this model can accurately reproduce the mean of the concentration--mass relation measured in N-body simulations, and reproduces more of the scatter in that relation than previous models. We further test this model by examining both the autocorrelation of scale radii across time, and the correlations between halo concentration and spin, and comparing to results measured from cosmological N-body simulations. In both cases we find that our model closely matches the N-body results. Our model is implemented within the open source Galacticus toolkit.
