论文标题
物理数据模型对偏见示踪剂初始条件的前进推断的影响
Impacts of the physical data model on the forward inference of initial conditions from biased tracers
论文作者
论文摘要
我们研究了每种成分在使用的物理数据模型中对贝叶斯向前条件的贝叶斯正向推断的影响，从野外示踪剂开始。具体而言，我们在给定的宇宙学模拟体积中使用暗物质光环作为基础物质密度场的示踪剂。我们研究示踪剂密度，电网分辨率，重力模型，偏置模型和可能性对推断初始条件的影响。我们发现，真实阶段和推断阶段之间的互相关系数对上述所有成分反应较弱，并且由从高斯模型中得出的理论期望得到很好的预测。另一方面，推断的初始条件的振幅偏见很大程度上取决于偏置模型和可能性。我们得出的结论是，偏见模型和可能性是无偏见的宇宙学推断的关键。他们必须将系统学保持在控制之下的亚网格物理学中，以获得无偏的推理。
We investigate the impact of each ingredient in the employed physical data model on the Bayesian forward inference of initial conditions from biased tracers at the field level. Specifically, we use dark matter halos in a given cosmological simulation volume as tracers of the underlying matter density field. We study the effect of tracer density, grid resolution, gravity model, bias model and likelihood on the inferred initial conditions. We find that the cross-correlation coefficient between true and inferred phases reacts weakly to all ingredients above, and is well predicted by the theoretical expectation derived from a Gaussian model on a broad range of scales. The bias in the amplitude of the inferred initial conditions, on the other hand, depends strongly on the bias model and the likelihood. We conclude that the bias model and likelihood hold the key to an unbiased cosmological inference. Together they must keep the systematics -- which arise from the sub-grid physics that are marginalized over -- under control in order to obtain an unbiased inference.
