论文标题
美元
${\rm S{\scriptsize IM}BIG}$: A Forward Modeling Approach To Analyzing Galaxy Clustering
论文作者
论文摘要
我们从新的$ {\ rm s {\ scriptssize im} big} $ forwart Modeling框架的新$ {\ rm s {\ rm s {\ rm s {\ rm s {\ rm s {\ rm s {\ rm s {\ rm s {\ rm s {\ rm} $ forward建模框架中介绍了第一个宇宙学约束。 $ {\ rm s {\ ScriptSize im} big} $利用了高保真模拟的预测能力，并提供了一个推理框架，可以在小型非线性尺度上提取宇宙学信息，而与标准分析无法访问。在这项工作中，我们将$ {\ rm s {\ scriptSize im} big} $应用于Boss Cmass Galaxy样本，并分析功率谱，$ p_ \ ell（k）$，$ k _ {\ rm max} = 0.5 \，h/{\ rm mpc} $。我们使用我们的正向模型构建20,000个模拟的星系样本，该模型基于高分辨率$ {\ rm q {\ scriptsize uijote}} $ $ n $ body模拟，并包括对观察性系统学的更完整处理的详细调查现实主义。然后，我们通过使用模拟样本训练归一化的流来进行SBI，并推断$λ$ CDM宇宙学参数的后验分布：$ω_m，ω_b，h，n_s，σ_8$。我们在$ω_m$和$σ_8$上获得了重大约束，这与以前的工作一致。我们对$σ_8$的限制比标准分析更精确。这种改进等同于通过分析$ \ sim60 \％$的星系样品比使用标准方法大的CMASS预期的统计增益。它是由于非线性量表超出当前分析模型极限的其他宇宙学信息的结果，$ k> 0.25 \，h/{\ rm mpc} $。尽管我们专注于这项工作中的$ p_ \ ell $，以验证和比较文献，但$ {\ rm s {\ scriptSize im} big} $提供了一个框架，用于使用任何摘要统计量来分析星系群集。我们希望从随后的$ {\ rm s {\ scriptsize im} big} $分析摘要统计信息的分析超过$ p_ \ ell $。
We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new ${\rm S{\scriptsize IM}BIG}$ forward modeling framework. ${\rm S{\scriptsize IM}BIG}$ leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small non-linear scales, inaccessible with standard analyses. In this work, we apply ${\rm S{\scriptsize IM}BIG}$ to the BOSS CMASS galaxy sample and analyze the power spectrum, $P_\ell(k)$, to $k_{\rm max}=0.5\,h/{\rm Mpc}$. We construct 20,000 simulated galaxy samples using our forward model, which is based on high-resolution ${\rm Q{\scriptsize UIJOTE}}$ $N$-body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of $Λ$CDM cosmological parameters: $Ω_m, Ω_b, h, n_s, σ_8$. We derive significant constraints on $Ω_m$ and $σ_8$, which are consistent with previous works. Our constraints on $σ_8$ are $27\%$ more precise than standard analyses. This improvement is equivalent to the statistical gain expected from analyzing a galaxy sample that is $\sim60\%$ larger than CMASS with standard methods. It results from additional cosmological information on non-linear scales beyond the limit of current analytic models, $k > 0.25\,h/{\rm Mpc}$. While we focus on $P_\ell$ in this work for validation and comparison to the literature, ${\rm S{\scriptsize IM}BIG}$ provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent ${\rm S{\scriptsize IM}BIG}$ analyses of summary statistics beyond $P_\ell$.
