标题： 宇宙学约束：使用超广域巡天望远镜第一年的Minkowski泛函数据 显示英文标题

标题： Cosmological constraints using Minkowski functionals from the first year data of the Hyper Suprime-Cam

摘要： 我们使用闵可夫斯基泛函来分析来自 Subaru Hyper Suprime-Cam (HSC-Y1) 第一年数据发布中的弱透镜汇聚图。闵可夫斯基泛函提供了对一个场的形态属性的描述，捕捉宇宙物质密度分布的非高斯特征。利用模拟目录来重现调查条件并编码宇宙学信息，我们在一系列宇宙学参数范围内模拟了闵可夫斯基泛函预测，从而从数据中得出最佳拟合值。通过应用多个尺度切割，我们严格减轻了系统效应，包括重子反馈和本征对齐。从分析中，结合角功率谱和闵可夫斯基泛函约束，我们得到$S_8 \equiv \sigma_8\sqrt{\Omega_{{\rm m}}/0.3} = {0.808}_{-0.046}^{+0.033}$和$\Omega_{\rm m} = {0.293}_{-0.043}^{+0.157}$。这些结果相比仅使用功率谱，对$S_8$的约束提高了$40\%$倍。\newtext 闵可夫斯基泛函的结果与其他使用相同数据的两点及更高阶统计约束一致，并与 Planck$S_8$测量的宇宙微波背景结果相符。 我们的研究展示了在约束 $\Omega_{\rm m}$ 和 $\sigma_8$ 之间的退化方面，闵可夫斯基泛函超越两点统计的强大功能。 显示英文摘要

摘要： We use Minkowski functionals to analyse weak lensing convergence maps from the first-year data release of the Subaru Hyper Suprime-Cam (HSC-Y1) survey. Minkowski functionals provide a description of the morphological properties of a field, capturing the non-Gaussian features of the Universe matter-density distribution. Using simulated catalogs that reproduce survey conditions and encode cosmological information, we emulate Minkowski functionals predictions across a range of cosmological parameters to derive the best-fit from the data. By applying multiple scales cuts, we rigorously mitigate systematic effects, including baryonic feedback and intrinsic alignments. From the analysis, combining constraints of the angular power spectrum and Minkowski functionals, we obtain $S_8 \equiv \sigma_8\sqrt{\Omega_{{\rm m}}/0.3} = {0.808}_{-0.046}^{+0.033}$ and $\Omega_{\rm m} = {0.293}_{-0.043}^{+0.157}$. These results represent a $40\%$ improvement on the $S_8$ constraints compared to using power spectrum only. \newtext{Minkowski functionals results are consistent with other two-point, and higher order statistics constraints using the same data, being in agreement with CMB results from the Planck $S_8$ measurements. Our study demonstrates the power of Minkowski functionals beyond two-point statistics to constrain and break the degeneracy between $\Omega_{\rm m}$ and $\sigma_8$.