论文标题

部分可观测时空混沌系统的无模型预测

Cosmological constraints from the density gradient weighted correlation function

论文作者

Xiao, Xiaoyuan, Yang, Yizhao, Luo, Xiaolin, Ding, Jiacheng, Huang, Zhiqi, Wang, Xin, Zheng, Yi, Sabiu, Cristiano G., Forero-Romero, Jaime, Miao, Haitao, Li, Xiao-Dong

论文摘要

标记加权相关函数(MCF)$ W(s,μ)$是一种计算有效的统计措施,可以探测超出传统2分统计信息的信息。在这项工作中,我们通过使用密度场梯度$ | \nablaρ/ρ|^α$的力量将传统的标记加权统计数据作为权重,并使用比例平均MCF的角度依赖性来限制宇宙学参数。分析表明,基于梯度的加权方案在统计上比基于密度的加权方案更强大,而将两个方案组合在一起比使用任何一个分别使用两个方案更强大。利用$α= 0.5,\ 1 $的加权或梯度加权MCF,我们可以分别通过2或4的因素来增强对$ω_m$的约束,而与标准的2点相关功能相比,同时使用两个加权方案的MCF同时使用的两个加权方案可以使用1.25 $倍的权重,而同时使用了均值$ 1.25 $倍的权重。商标加权统计数据可能在对未来大规模调查的宇宙学分析中起重要作用。许多问题,包括使用其他类型的权重的可能性,偏见对此统计数据的影响以及MCF在断层扫描Alcock-Paczynski方法中的使用,值得进一步研究。

The mark weighted correlation function (MCF) $W(s,μ)$ is a computationally efficient statistical measure which can probe clustering information beyond that of the conventional 2-point statistics. In this work, we extend the traditional mark weighted statistics by using powers of the density field gradient $|\nabla ρ/ρ|^α$ as the weight, and use the angular dependence of the scale-averaged MCFs to constrain cosmological parameters. The analysis shows that the gradient based weighting scheme is statistically more powerful than the density based weighting scheme, while combining the two schemes together is more powerful than separately using either of them. Utilising the density weighted or the gradient weighted MCFs with $α=0.5,\ 1$, we can strengthen the constraint on $Ω_m$ by factors of 2 or 4, respectively, compared with the standard 2-point correlation function, while simultaneously using the MCFs of the two weighting schemes together can be $1.25$ times more statistically powerful than using the gradient weighting scheme alone. The mark weighted statistics may play an important role in cosmological analysis of future large-scale surveys. Many issues, including the possibility of using other types of weights, the influence of the bias on this statistics, as well as the usage of MCFs in the tomographic Alcock-Paczynski method, are worth further investigations.

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