AI 中文总结
该研究提出混合晕模型CHEFT，通过构建塌缩框架改进双晕项描述，利用概率偏差方法推导偏差参数，提供无自由偏差参数的预测模型，验证表明其在物质场和加权示踪剂方面有高精度，保留EFT精度与灵活性。
AI 中文摘要
我们提出了一种混合晕模型，通过纳入模拟中的非线性信息来改进双晕项的描述。晕-晕功率谱的线性计算在单晕和双晕区域之间的过渡处不准确，而非线性方法如混合有效场论（HEFT）与晕模型分解不自然兼容。我们通过构建一个塌缩的HEFT（CHEFT）框架来解决这一限制，其中HEFT算符展开的功率谱模板是从通过将粒子塌缩到晕中心来去除单晕贡献的模拟中测量的。晕-晕功率谱然后表示为偏差算符的总和，使用概率偏差方法从模拟中推导出与质量相关的偏差参数。这提供了一个没有自由偏差参数的预测模型。我们针对一系列旨在模拟天体物理可观测量的晕质量依赖性的加权方案验证了该模型，包括太阳亚耶夫-泽尔多维奇效应、宇宙红外背景以及通过晕占据分布描述的星系丰度。对于物质场，该模型在整个过渡区域将功率谱恢复到百分比水平的精度。对于加权示踪剂，基线模型在功率方面达到约5-10%的精度，当在偏差展开中包括有效的高阶导数、类似拉普拉斯的贡献时，精度提高到约3-5%的水平。因此，CHEFT模型保留了EFT方法的精度和灵活性，同时允许透明地纳入与晕直接相关的天体物理效应。
英文摘要
We present a hybrid halo model, which improves the description of the 2-halo term by incorporating non-linear information from simulations. A linear computation of the halo-halo power spectrum is inaccurate at the transition between the 1-halo and 2-halo regimes, whereas nonlinear approaches such as Hybrid Effective Field Theory (HEFT) are not naturally compatible with the halo model decomposition. We address this limitation by constructing a collapsed HEFT (CHEFT) framework, in which the power-spectrum templates of the HEFT operator expansion are measured from simulations where 1-halo contributions are removed by collapsing particles to their halo centres. The halo-halo power spectrum is then expressed as a sum over bias operators, with mass-dependent bias parameters deduced from simulation using the probabilistic bias approach. This provides a predictive model in which there are no free bias parameters. We validate the model for a range of weighting schemes designed to mimic the halo-mass dependence of astrophysical observables, including the Sunyaev-Zeldovich effect, the Cosmic Infrared Background, and galaxy abundances described via a halo occupation distribution. For the matter field, the model recovers the power spectrum to percent-level accuracy across the transition regime. For weighted tracers, the baseline model achieves accuracies of $\sim 5-10\%$ in power, which improves to the $\sim 3-5\%$ level when including an effective higher-derivative, Laplacian-like contribution in the bias expansion. The CHEFT model thus retains the precision and flexibility of the EFT approach, while allowing the transparent incorporation of astrophysical effects that are directly associated with haloes.
Comments18 pages, 11 figures, comments welcome
