论文标题
基于地图的宇宙学推断对数正常宇宙剪切图
Map-based cosmology inference with lognormal cosmic shear maps
论文作者
论文摘要
迄今为止，大多数宇宙剪切分析都依赖于摘要统计信息（例如$ξ_+$和$ξ_- $）。这些类型的分析必然是优化的，因为摘要统计数据的使用是有损的。在本文中，我们将宇宙的收敛场作为对数正态的随机场进行转发模型。这个新的基于地图的推理框架使我们能够恢复宇宙参数和宇宙收敛场的关节后部。我们的分析适当说明了整个层析成像箱中质量图的协方差，这显着提高了地图相对于单键重建的忠诚度。我们验证，将推理管道应用于高斯随机字段会恢复与他们的分析对应物非常吻合的后代。在我们的地图的分辨率上 - 在融合字段可以通过对数正态模型描述的范围内 - 我们的地图后代使我们能够重建所有\ rm摘要统计量（包括非高斯统计量）。我们预测，基于地图的LSST-Y10数据的推理分析可以改善$σ_8$ - $ω_ {\ rm m} $平面在$σ_8$ - $ \％的情况下，相对于当前标准的宇宙剪切分析。这种改进几乎完全沿$ s_8 =σ_8Ω_ {\ rm m}^{1/2} $方向进行，这意味着基于地图的推理无法显着改善$ S_8 $的约束。
Most cosmic shear analyses to date have relied on summary statistics (e.g. $ξ_+$ and $ξ_-$). These types of analyses are necessarily sub-optimal, as the use of summary statistics is lossy. In this paper, we forward-model the convergence field of the Universe as a lognormal random field conditioned on the observed shear data. This new map-based inference framework enables us to recover the joint posterior of the cosmological parameters and the convergence field of the Universe. Our analysis properly accounts for the covariance in the mass maps across tomographic bins, which significantly improves the fidelity of the maps relative to single-bin reconstructions. We verify that applying our inference pipeline to Gaussian random fields recovers posteriors that are in excellent agreement with their analytical counterparts. At the resolution of our maps -- and to the extent that the convergence field can be described by the lognormal model -- our map posteriors allow us to reconstruct \it all \rm summary statistics (including non-Gaussian statistics). We forecast that a map-based inference analysis of LSST-Y10 data can improve cosmological constraints in the $σ_8$--$Ω_{\rm m}$ plane by $\approx 30\%$ relative to the currently standard cosmic shear analysis. This improvement happens almost entirely along the $S_8=σ_8Ω_{\rm m}^{1/2}$ directions, meaning map-based inference fails to significantly improve constraints on $S_8$.
