标题： 用混合SBI建模星系调查 显示英文标题

标题： Modeling Galaxy Surveys with Hybrid SBI

摘要： 基于模拟的推理（SBI）已成为从星系巡天中提取宇宙信息的强大工具，深入到了非线性区域。 尽管前景广阔，但由于运行能够描述日益增长的宇宙数据集的模拟所需的计算成本，其应用受到限制。 近期的工作提出了一种混合SBI框架（HySBI），该框架结合了小尺度上的SBI和大尺度上的扰动理论（PT），允许从高分辨率观测中提取信息，而无需大规模的模拟。 在这项工作中，我们提出了用于星系聚类的HySBI框架，这是将其应用于下一代数据集的关键一步。 我们研究了PT分析和基于模拟分析中建模星系参数的先验选择，以及它们对宇宙学的依赖性。 通过联合建模大尺度和小尺度统计及其相关的系统参数，我们展示了HySBI相比传统PT分析可以分别在$\Omega_m$和$\sigma_8$上获得20%和60%更紧的约束，从而证明了这种方法在最大限度地从即将进行的光谱数据集中提取信息方面的有效性。 显示英文摘要

摘要： Simulation-based inference (SBI) has emerged as a powerful tool for extracting cosmological information from galaxy surveys deep into the non-linear regime. Despite its great promise, its application is limited by the computational cost of running simulations that can describe the increasingly-large cosmological datasets. Recent work proposed a hybrid SBI framework (HySBI), which combines SBI on small-scales with perturbation theory (PT) on large-scales, allowing information to be extracted from high-resolution observations without large-volume simulations. In this work, we lay out the HySBI framework for galaxy clustering, a key step towards its application to next-generation datasets. We study the choice of priors on the parameters for modeling galaxies in PT analysis and in simulation-based analyses, as well as investigate their cosmology dependence. By jointly modeling large- and small-scale statistics and their associated nuisance parameters, we show that HySBI can obtain 20\% and 60\% tighter constraints on $\Omega_m$ and $\sigma_8$, respectively, compared to traditional PT analyses, thus demonstrating the efficacy of this approach to maximally extract information from upcoming spectroscopic datasets.