AI 中文总结
研究提出网络晕轮模型峰-背景分割（WHM-PBS）理论，利用宇宙网络环境生成晕轮偏差分布。通过三种方式应用该分布，解释了密度偏差关系、晕轮随机性及偏差-浓度-相关性反转，无需针对组装偏差调参。
AI 中文摘要
我们提出了网络晕轮模型峰-背景分割（WHM-PBS），这是一种解析理论，其中暗物质晕轮的大尺度偏差源自其宇宙网络环境。基于网络晕轮模型，我们采用Shen等人的椭圆塌缩移动壁垒来生成网络层级结构，每个晕轮都位于宿主细丝内，而宿主细丝又位于薄片内。结合峰-背景分割，此图景用宿主环境的偏差取代了确定性的偏差-质量关系\(b(M_h)\)，并在条件质量函数上进行平均。结果，晕轮偏差\(b(M_h)\)不再是一个数值，而是一个高度倾斜的分布。在这项工作中，我们以三种不同方式利用此分布：一是作为偏差关系的物理动机先验；二是对晕轮随机性的预测；三是组装偏差模型的框架。对于（i），我们发现密度偏差关系\(b_2(b_1)\)和\(b_3(b_1)\)保持紧密，而潮汐偏差\(b_{s^2}(b_1)\)显示出显著散射，正如在N体模拟中发现的那样。对于（ii），一旦纳入晕轮排除，我们的模型再现了Baldauf等人的超泊松到亚泊松散粒噪声趋势，我们将其转换为有效场论随机性振幅参数的先验带。最后，对于（iii），在密度领域，它解释了Paranjape等人在特征质量（\(M_h\simeq1.7\times10^{13}\,h^{-1}\Msun\)）处的偏差-浓度-相关性反转，且未针对组装偏差调整任何参数。
英文摘要
We present the Web-Halo Model Peak-Background Split (WHM-PBS), an analytic theory in which the large-scale bias of a dark-matter halo is inherited from its cosmic-web environment. Building on the Web-Halo Model, we use the Shen et al. moving barriers for ellipsoidal collapse generating the web hierarchy in which every halo sits inside a host filament, itself inside a sheet. Combined with the peak-background split, this picture replaces the deterministic bias-mass relation $b(M_h)$ with the bias of the host environment, averaged over the conditional mass function. As a result, halo bias $b(M_h)$ is no longer a number but a strongly skewed distribution. In this work we make use of this distribution in three different ways: as (i) a physically motivated prior on bias relations, (ii) a prediction on halo stochasticity, and (iii) a framework for assembly bias models. Regarding (i) we find that the density bias relations $b_2(b_1)$ and $b_3(b_1)$ stay tight, while the tidal bias $b_{s^2}(b_1)$ shows significant scatter, as found in $N$-body simulations. Regarding (ii), once including halo exclusion, our model reproduces the super- to sub-Poisson shot-noise trend of Baldauf et al. which we convert into a prior band on the EFT stochasticity amplitude parameters. Finally, regarding (iii) in the density sector it explains the bias-concentration-correlation inversion of Paranjape et al. at the characteristic mass ($M_\mathrm{h}\simeq 1.7\times 10^{13}\,h^{-1} M_\odot$), with no parameter tuned to assembly bias. We apply the resulting priors on synthetic data, demonstrating that the WHM-PBS priors mitigate projection effects arising when all nuisance parameters are varied freely, while fixed priors may fail severely. The code to reproduce our results is publicly available at https://github.com/SamuelBrieden/whmpbs.git.
Comments48 pages, 13 figures, 3 tables, v2 matches journal-submitted version
