在千兆秒差距尺度上实现宇宙网分析：一种用于DisPerSE的多块方法
Enabling Cosmic Web Analysis at Gigaparsec Scales: A Multi Block Approach for DisPerSE
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中文总结 AI 辅助
研究旨在解决DisPerSE在千兆秒差距尺度分析宇宙网时的内存瓶颈问题。提出冻结核心多块方法，经验证效果良好。应用该方法生成丝状结构目录，并首次在N体晕目录中证实理论预测的质量 - 连通性关系在晕质量多数量级上成立。
中文摘要 AI 辅助
宇宙丝状结构是宇宙中最长的结构，也是宇宙网的主导元素，它引导物质进入星系团并塑造星系形成和演化的环境。在千兆秒差距体积上精确重建这个网络对宇宙学和星系演化越来越重要。然而，最常用的拓扑丝状结构探测器DisPerSE面临内存瓶颈，因为它需要对整个输入点集进行德劳内三角剖分，这使得它无法应用于大型模拟。简单地分割体积会失败，因为不同子体积会产生不一致的三角剖分和丝状网络。我们提出了一种冻结核心方法，该方法克服了这个瓶颈，同时保留了全局拓扑结构。体积被分解为重叠块，其三角剖分通过外接球标准进行过滤，只保留全局有效的四面体；后处理管道通过核心过滤、去重和边界拼接来合并平铺输出。在一个300 h⁻¹ Mpc的MDPL2子体积上与整体参考进行验证，结果显示总长度恢复率为99.6%，密度最大值和最小值恢复率为100%，单个丝状结构匹配率为94.7%（约5%未匹配的丝状结构主要是短的、低显著性结构）。我们将该方法应用于完整的(1 h⁻¹ Gpc)³ MDPL2盒子（9200万个晕），生成了一个千兆秒差距尺度的丝状结构目录。作为第一个应用，我们测量了22900个跨越M₂₀₀c = 10¹² - 10¹⁵.⁵ h⁻¹ M⊙的晕的连通性（κ），发现了一个从星系群到星系团尺度的幂律质量 - 连通性关系，这在N体晕目录中首次证实了理论预测的标度在晕质量的三个数量级上成立。
英文摘要
Cosmic filaments are the longest structures in the Universe and the dominant element of the cosmic web, channelling matter onto clusters and shaping the environments in which galaxies form and evolve. Accurate reconstructions of this network across gigaparsec volumes are increasingly important for cosmology and galaxy evolution. However, the most commonly used topological filament finder, DisPerSE (Discrete Persistent Structures Extractor), faces a memory bottleneck: it requires a Delaunay tessellation of the full input point set, preventing application to large simulations. Naively splitting the volume fails, as different sub-volumes yield inconsistent tessellations and filament networks. We present a frozen-core method that overcomes this bottleneck while preserving the global topology. The volume is decomposed into overlapping blocks whose tessellations are filtered by a circumsphere criterion retaining only globally valid tetrahedra; a post-processing pipeline merges the tiled outputs through core filtering, deduplication, and boundary stitching. Validation against a monolithic reference on a $300\,h^{-1}\,\mathrm{Mpc}$ MDPL2 subvolume shows 99.6% total length recovery, 100% recovery of density maxima and minima, and 94.7% individual filament matching (the ${\sim}$5% of unmatched filaments are predominantly short, low-significance structures). We apply the method to the full $(1\,h^{-1}\,\mathrm{Gpc})^3$ MDPL2 box (92 million haloes), producing a gigaparsec-scale filament catalogue. As a first application, we measure the connectivity ($κ$) for 22,900 haloes spanning $M_{200\mathrm{c}} = 10^{12}$-$10^{15.5}\,h^{-1}\,\mathrm{M}_\odot$, finding a power-law mass-connectivity relation that extends from group to cluster scales, providing the first confirmation in an $N$-body halo catalogue that the theoretically predicted scaling holds across three decades in halo mass.
