AI 中文总结
研究利用有效场理论分析21厘米强度图时氢碘偏差参数先验问题，提出基于模拟框架学习条件分布，用信息性先验取代宽泛先验，训练归一化流得到结构化映射，产生更紧密先验，且不同模拟结果有差异，为相关调查提供初步步骤。
AI 中文摘要
利用大规模结构的有效场理论对21厘米强度图进行全形状分析，需要氢碘偏差参数的先验信息，而宽泛的无信息先验可能导致宇宙学约束过于保守。本文提出一个基于模拟的框架，通过学习基于有效场理论的偏差参数与非线性区域氢碘聚类模型参数之间的条件分布，用信息性先验取代宽泛先验。具体通过对隐藏山谷模拟应用简单的氢碘晕占据分布，训练关于最低阶局部偏差参数和潮汐偏差的条件归一化流。结果表明，得到的晕占据分布到偏差的映射高度结构化，对晕质量幂次有强烈依赖。通过此映射传播类CHORD望远镜对非线性尺度上21厘米功率谱的灵敏度预测，产生的非高斯、相关先验比传统平坦先验更紧密。使用IllustrisTNG模拟的晕目录重复分析，发现与隐藏山谷结果有不可忽略的差异。该框架为当前和即将进行的氢碘强度映射调查提供了迈向信息性有效场理论先验的初步步骤。
英文摘要
Full-shape analyses of 21 cm intensity maps with the effective field theory of large-scale structure will require priors on HI bias parameters, and the standard choice of broad uninformative priors can lead to cosmological constraints that are unnecessarily conservative. We present a simulation-based framework that replaces these broad priors with informative priors based on learning the conditional distribution $p(\bmθ_{\rm EFT}\mid\bmθ_{\rm HOD})$ between effective-field-theory-based bias parameters and the parameters of a model for HI clustering in the nonlinear regime. Specifically, we train a conditional normalizing flow on field-level measurements of the lowest-order local bias parameters $(b_1,b_2,b_3)$ and the tidal bias $b_{\mathcal{G}_2}$ by applying a simple HI halo occupation distribution (HOD) to the Hidden Valley simulations. We find that the resulting HOD-to-bias mapping is highly structured, displaying a strong dependence on the power of halo mass in the HOD model. Propagating CHORD-like telescope sensitivity forecasts for the 21 cm power spectrum on nonlinear scales through this mapping produces non-Gaussian, correlated priors on the bias parameters that are substantially tighter than conventional flat priors across $z=1$--$3$, with the improvement most dramatic at high redshift. By repeating our analysis using halo catalogs from the IllustrisTNG simulations, we find non-negligible differences from the Hidden Valley results, indicating that future applications of simulation-based HI priors will need to carefully account for the dependence of these priors on the simulations used to construct them. Our framework provides an initial step toward informative EFT priors for current and forthcoming HI intensity mapping surveys, including CHIME, CHORD, and MeerKLASS.
Comments29 pages, 29 figures, 1 table; comments are welcome
