论文标题
光环三轴性的随机步行模型
A Random Walk Model for Halo Triaxiality
论文作者
论文摘要
我们描述了一个半分析模型，以通过在合并树中捕获的随机合并事件的序列来预测暗物质光环的三轴形状，以遵循每个光环的能量张量的演变。当结合一个简单的模型以朝向球形形状放松时，我们发现该模型预测了光环轴长度比的分布，该分布与曾经限制在单个光晕质量下匹配的宇宙N体仿真曾经与中位轴比相匹配的模型。我们通过考虑轴长度比的条件分布以及光环形状的质量依赖性来证明该模型的预测和解释能力，发现这些分布与N体结果非常吻合。该模型既可以洞悉驱动光环三轴形状进化的物理学，又提供了对直接连接到光环的形成历史的三轴性统计量的快速定量预测。
We describe a semi-analytic model to predict the triaxial shapes of dark matter halos utilizing the sequences of random merging events captured in merger trees to follow the evolution of each halo's energy tensor. When coupled with a simple model for relaxation toward a spherical shape, we find that this model predicts distributions of halo axis length ratios which approximately agree with those measured from cosmological N-body simulations once constrained to match the median axis ratio at a single halo mass. We demonstrate the predictive and explanatory power of this model by considering conditioned distributions of axis length ratios, and the mass-dependence of halo shapes, finding these to be in good agreement with N-body results. This model provides both insight into the physics driving the evolution of halo triaxial shapes, and rapid quantitative predictions for the statistics of triaxiality connected directly to the formation history of the halo.
