论文标题

用密度分解聚类来限制νλCDM

Constraining νΛCDM with density-split clustering

论文作者

Paillas, Enrique, Cuesta-Lazaro, Carolina, Zarrouk, Pauline, Cai, Yan-Chuan, Percival, Will J., Nadathur, Seshadri, Pinon, Mathilde, de Mattia, Arnaud, Beutler, Florian

论文摘要

星系聚类对局部密度的依赖性提供了一种从星系调查中提取非高斯信息的有效方法。两点相关函数(2PCF)提供了高斯密度场的完整统计描述。但是,由于非线性引力演化,延迟密度场成为非高斯,并且需要高阶汇总统计数据才能捕获其所有宇宙学信息。使用基于Quijote模拟的Halo目录的Fisher形式主义,我们探讨了使用密度分解聚类(DS)方法检索此信息的可能性,该方法结合了来自不同环境密度区域的聚类统计数据。我们表明,与2PCF相比,DS对$νλ$ CDM模型的参数提供了更精确的约束,我们为额外信息的来源提供建议。 DS将中微子质量总和的约束提高了$ 7 $,以及4、3、3、6和5的因素,分别为$ω_ {\ rm m} $,$ω_ {\ rm b} $,$ h $,$ h $,$ h $,$ n_s $和$ n_s $和$σ_8$。当从光环的实际或红移空间位置估算局部密度环境时,我们比较DS统计数据。除了DS环境和光环之间的互相关功能外,还包括DS自相关功能,还恢复了使用红移空位位置估算环境时丢失的大多数信息。我们讨论了在不同情况下构建基于仿真的方法来对DS聚类统计量建模的可能性。

The dependence of galaxy clustering on local density provides an effective method for extracting non-Gaussian information from galaxy surveys. The two-point correlation function (2PCF) provides a complete statistical description of a Gaussian density field. However, the late-time density field becomes non-Gaussian due to non-linear gravitational evolution and higher-order summary statistics are required to capture all of its cosmological information. Using a Fisher formalism based on halo catalogues from the Quijote simulations, we explore the possibility of retrieving this information using the density-split clustering (DS) method, which combines clustering statistics from regions of different environmental density. We show that DS provides more precise constraints on the parameters of the $νΛ$CDM model compared to the 2PCF, and we provide suggestions for where the extra information may come from. DS improves the constraints on the sum of neutrino masses by a factor of $7$ and by factors of 4, 3, 3, 6, and 5 for $Ω_{\rm m}$, $Ω_{\rm b}$, $h$, $n_s$, and $σ_8$, respectively. We compare DS statistics when the local density environment is estimated from the real or redshift-space positions of haloes. The inclusion of DS autocorrelation functions, in addition to the cross-correlation functions between DS environments and haloes, recovers most of the information that is lost when using the redshift-space halo positions to estimate the environment. We discuss the possibility of constructing simulation-based methods to model DS clustering statistics in different scenarios.

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