三维莱曼-α森林的最小有效场论的物理校准
Physical Calibration of a Minimal Effective Field Theory of the Three-Dimensional Lyman-$α$ Forest
AI总结：
该研究利用Sherwood系列模拟，校准三维莱曼-α森林的最小有效场论模型，分析其功率谱、参数简并及各因素对参数的影响，并与解析模型预测对比。
AI中文摘要：
我们使用Sherwood和Sherwood--Relics流体动力学模拟，研究三维莱曼-α森林的最小有效场论描述。我们采用树级偏置模型，结合主要的抵消项和随机贡献，对莱曼-α流量自功率谱及其与暗物质密度场的交叉相关功率谱进行建模。我们发现，该模型能分别在$k_{\mathrm{max}}=3\\,h\\,{\rm Mpc}^{-1}$和$k_{\mathrm{max}}=2\\,h\\,{\rm Mpc}^{-1}$范围内很好地描述模拟的自功率谱和交叉功率谱。我们分析了覆盖多个红移、再电离历史、模拟盒大小和分辨率的模拟结果，以评估模型的稳健性。即使在这个最小模型内，我们也发现了强烈的参数简并，强调了在实际数据分析应用中需要对冗余参数进行独立约束。线性偏置参数的红移演化与之前基于模拟的研究一致，且主要由有效光深$\tau_{\mathrm{eff}}$的演化驱动。我们还发现，推断出的参数受模拟分辨率的影响，受模拟盒大小的影响较小。我们还探索了再电离历史的影响，发现变化主要影响所考虑尺度范围内的线性偏置参数。此外，我们发现模型参数与莱曼-α森林密度偏置之间存在经验相关性，且存在一定分散，这表明单个参数不足以确定模型参数。最后，我们将结果与莱曼-α森林解析模型的理论预测进行了比较。
英文摘要：
We study a minimal effective field theory description of the three-dimensional Lyman-$α$ forest using the Sherwood and Sherwood--Relics hydrodynamical simulations. We model the Lyman-$α$ flux auto-power spectrum and its cross-correlation power spectrum with the dark matter density field using a tree-level bias model supplemented by the leading counterterms and stochastic contributions. We find that the model describes the simulated auto- and cross-power spectra well up to $k_{\mathrm{max}}=3\,h\,{\rm Mpc}^{-1}$ and $k_{\mathrm{max}}=2\,h\,{\rm Mpc}^{-1}$, respectively. We analyse simulations spanning multiple redshifts, reionisation histories, box sizes and resolutions to assess the robustness of the model. Even within this minimal model, we find strong parameter degeneracies, highlighting the need for independent constraints on nuisance parameters in applications to real data analyses. The redshift evolution of the linear bias parameters is consistent with previous simulation-based studies and is driven primarily by the evolution of the effective optical depth, $τ_{\mathrm{eff}}$. We also find that the inferred parameters are affected by the resolution of the simulation and, to a lesser extent, by the simulation box size. We also explore the impact of reionisation history, finding that variations mainly affect the linear bias parameters over the range of scales considered. Moreover, we find empirical correlations between model parameters and the Lyman-$α$ forest density bias that show some scatter, indicating that one single parameter is not enough to determine the model parameters. Finally, we compare our results with theoretical predictions from analytical models of the Lyman-$α$ forest.
