论文标题

来自弱透镜收敛PDF的NUW CDM宇宙学

Nuw CDM cosmology from the weak lensing convergence PDF

论文作者

Boyle, Aoife, Uhlemann, Cora, Friedrich, Oliver, Barthelemy, Alexandre, Codis, Sandrine, Bernardeau, Francis, Giocoli, Carlo, Baldi, Marco

论文摘要

固定总中微子质量和状态的暗能量方程是即将进行星系调查的关键目的。弱透镜是对总物质分布的独特探针,其非高斯统计数据可以通过透镜收敛的单点概率分布函数(PDF)来量化。我们使用大型驱动统计数据计算出轻度非线性尺度上的收敛性PDF,考虑到暗能量和总中微子质量。我们首次全面验证了收敛PDF模型的宇宙学依赖性,以模拟镜头图的大型套件,以证明其百分比的精度和准确性。我们表明,快速模拟代码可以提供高度准确的协方差矩阵,可以将其与理论PDF模型结合使用,以进行预测并消除依靠昂贵的N体模拟的需求。我们的理论模型使我们能够对$λ$ cdm参数的完整集的收敛性PDF执行第一个预测。我们的Fisher预测表明,收敛性PDF的约束功率与单个源红移的欧几里得样调查区域的两点相关函数相比。当与普朗克的CMB先验结合使用时,PDF既约束中微子质量$m_ν$又约束状态$ W_0 $的暗能量方程,而不是两点相关函数。

Pinning down the total neutrino mass and the dark energy equation of state is a key aim for upcoming galaxy surveys. Weak lensing is a unique probe of the total matter distribution whose non-Gaussian statistics can be quantified by the one-point probability distribution function (PDF) of the lensing convergence. We calculate the convergence PDF on mildly non-linear scales from first principles using large-deviation statistics, accounting for dark energy and the total neutrino mass. For the first time, we comprehensively validate the cosmology-dependence of the convergence PDF model against large suites of simulated lensing maps, demonstrating its percent-level precision and accuracy. We show that fast simulation codes can provide highly accurate covariance matrices, which can be combined with the theoretical PDF model to perform forecasts and eliminate the need for relying on expensive N-body simulations. Our theoretical model allows us to perform the first forecast for the convergence PDF that varies the full set of $Λ$CDM parameters. Our Fisher forecasts establish that the constraining power of the convergence PDF compares favourably to the two-point correlation function for a Euclid-like survey area at a single source redshift. When combined with a CMB prior from Planck, the PDF constrains both the neutrino mass $M_ν$ and the dark energy equation of state $w_0$ more strongly than the two-point correlation function.

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