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arXiv 2407.06641astro-ph.COgr-qc
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尺度依赖的原初非高斯性的宇宙学模拟

Cosmological simulations of scale-dependent primordial non-Gaussianity

Marco Baldi, Emanuele Fondi, Dionysios Karagiannis, Lauro Moscardini, Andrea Ravenni, William R. Coulton, Gabriel Jung, Michele Liguori, Marco Marinucci, Licia … 展开作者

Marco Baldi, Emanuele Fondi, Dionysios Karagiannis, Lauro Moscardini, Andrea Ravenni, William R. Coulton, Gabriel Jung, Michele Liguori, Marco Marinucci, Licia Verde, Francisco Villaescusa-Navarro, Banjamin D. Wandelt

AI总结:

本研究开展了含局域型尺度依赖原初非高斯性的ΛCDM宇宙学N体模拟,验证其符合宇宙微波背景限制的同时,揭示了其对多种宇宙结构性质的影响,并首次发现部分模型可模拟温暗物质的可观测量效应。

摘要:

我们给出了一组采用标准ΛCDM宇宙学模型的宇宙学N体模拟结果,该模型的特征是局域型尺度依赖原初非高斯性,其$f_{\ m NL}^{\ m loc}(k)$在大尺度上呈幂律依赖关系,在小尺度上则饱和为恒定值——小尺度下的非线性增长会导致坍缩宇宙结构的形成。构建这类模型是为了确保其与当前宇宙微波背景(Cosmic Microwave Background)对原初非高斯性的限制相一致,同时又能让非高斯统计特性对非线性结构形成的性质产生显著影响。我们展示了这类尺度依赖非高斯情形对所产生宇宙结构的多种性质的影响,包括非线性物质功率谱、暗物质晕与子晕质量函数、浓度-质量关系、暗物质晕与空洞密度轮廓,并首次指出其中部分模型可能在多个这类可观测量上模拟温暗物质(Warm Dark Matter)的效应。

英文摘要:

We present the results of a set of cosmological N-body simulations with standard $Λ$CDM cosmology but characterized by a scale-dependent primordial non-Gaussianity of the local type featuring a power-law dependence of the $f_{\rm NL}^{\rm loc}(k)$ at large scales followed by a saturation to a constant value at smaller scales where non-linear growth leads to the formation of collapsed cosmic structures. Such models are built to ensure consistency with current Cosmic Microwave Background bounds on primordial non-Gaussianity yet allowing for large effects of the non-Gaussian statistics on the properties of non-linear structure formation. We show the impact of such scale-dependent non-Gaussian scenarios on a wide range of properties of the resulting cosmic structures, such as the non-linear matter power spectrum, the halo and sub-halo mass functions, the concentration-mass relation, the halo and void density profiles, and we highlight for the first time that some of these models might mimic the effects of Warm Dark Matter for several of such observables

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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