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arXiv 2403.13985astro-ph.COhep-thmath.AT
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基于持续同调的宇宙学研究:Fisher 预测

Cosmology with Persistent Homology: a Fisher Forecast

Jacky H. T. Yip, Matteo Biagetti, Alex Cole, Karthik Viswanathan, Gary Shiu

AI总结:

本研究将持续同调应用于Quijote模拟的暗物质晕星表,通过Fisher分析发现其对多数宇宙学参数的约束优于联合功率谱与双谱统计量,二者结合可进一步提升约束能力,为该方法纳入宇宙学数据推断流程提供依据。

摘要:

持续同调可自然表征大尺度结构作为团簇、环和空洞分布的多尺度拓扑特征。我们将该工具应用于 Quijote 模拟得到的暗物质晕星表,构建了一种汇总统计量,并将其与联合功率谱及双谱统计量在宇宙学参数和原初非高斯性的信息含量方面进行对比。通过 Fisher 分析,我们发现在10个参数中,持续同调对8个参数的约束更严格,约束提升幅度为13%至50%。两种统计量的互补性打破了参数简并,联合使用时可进一步提升约束能力。我们开展了一系列一致性检验以巩固结果,结论表明,本研究的发现为将持续同调纳入宇宙学巡天数据的推断流程提供了依据。

英文摘要:

Persistent homology naturally addresses the multi-scale topological characteristics of the large-scale structure as a distribution of clusters, loops, and voids. We apply this tool to the dark matter halo catalogs from the Quijote simulations, and build a summary statistic for comparison with the joint power spectrum and bispectrum statistic regarding their information content on cosmological parameters and primordial non-Gaussianity. Through a Fisher analysis, we find that constraints from persistent homology are tighter for 8 out of the 10 parameters by margins of 13-50%. The complementarity of the two statistics breaks parameter degeneracies, allowing for a further gain in constraining power when combined. We run a series of consistency checks to consolidate our results, and conclude that our findings motivate incorporating persistent homology into inference pipelines for cosmological survey data.

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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