Enhancing Morphological Measurements of the Cosmic Web with Delaunay Tessellation Field Estimation

Yu Liu, Yu Yu, Pengjie Zhang, Hao-Ran Yu
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Abstract

The density fields constructed by traditional mass assignment methods are susceptible to irritating discreteness, which hinders morphological measurements of cosmic large-scale structure (LSS) through Minkowski functionals (MFs). To alleviate this issue, fixed-kernel smoothing methods are commonly used in the literature, at the expense of losing substantial structural information. In this work, we propose to measure MFs with the Delaunay tessellation field estimation (DTFE) technique, with the goal of maximizing the extraction of morphological information from sparse tracers. We perform our analyses starting from matter fields and progressively extending to halo fields. At the matter-field level, we elucidate how discreteness affects morphological measurements of LSS. Then, by comparing with the traditional Gaussian smoothing scheme, we preliminarily showcase the advantages of DTFE for enhancing measurements of MFs from sparse tracers. At the halo-field level, we first numerically investigate various systematic effects on MFs of DTFE fields, which are induced by finite voxel sizes, halo number densities, halo weightings, and redshift space distortions (RSDs), respectively. Then, we explore the statistical power of MFs measured with DTFE for extracting the cosmological information encoded in RSDs. We find that MFs measured with DTFE exhibit improvements by ∼2 orders of magnitude in discriminative power for RSD effects and by a factor of ∼3–5 in constraining power on the structure growth rate over the MFs measured with Gaussian smoothing. These findings demonstrate the remarkable enhancements in statistical power of MFs achieved by DTFE, showing enormous application potentials for our method in extracting various key cosmological information from galaxy surveys.
利用德劳内细分场估计增强宇宙网的形态测量
传统质量分配方法构建的密度场容易受到离散性的影响,从而阻碍了通过闵科夫斯基函数(MFs)对宇宙大尺度结构(LSS)进行形态学测量。为了缓解这一问题,文献中通常采用固定核平滑方法,但这种方法会损失大量结构信息。在这项工作中,我们建议使用德劳内细分场估计(DTFE)技术测量 MFs,目的是从稀疏描记物中最大限度地提取形态信息。我们的分析从物质场开始,逐步扩展到晕场。在物质场层面,我们阐明了离散性如何影响 LSS 的形态测量。然后,通过与传统高斯平滑方案的比较,我们初步展示了 DTFE 在增强稀疏示踪剂 MF 测量方面的优势。在晕场层面,我们首先对 DTFE 场的 MFs 的各种系统性影响进行了数值研究,这些影响分别由有限体素尺寸、晕数密度、晕权重和红移空间扭曲(RSD)引起。然后,我们探讨了用 DTFE 测量的 MFs 在提取 RSDs 中编码的宇宙学信息方面的统计能力。我们发现,与高斯平滑测量的 MF 相比,用 DTFE 测量的 MF 对 RSD 效应的判别能力提高了 2 个数量级,对结构增长率的约束能力提高了 3-5 倍。这些发现表明,DTFE 显著提高了 MFs 的统计能力,显示了我们的方法在从星系巡天中提取各种关键宇宙学信息方面的巨大应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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