Hermes——迈向用于大数据集宇宙统计的最优高性能算法
Hermes - Towards an Optimal High-Performance Algorithm for Cosmic Statistics of Large Data Sets
AI总结：
研究提出Hermes框架用于宇宙大数据统计，通过多分辨率重建等技术，结合代数运算替代粒子元组计数，利用PyHermes实现多种统计量测量，测试表明其效率高、可扩展，适合星系巡天大数据集。
AI中文摘要：
我们展示了Hermes，这是一个原位多分辨率框架，用于从离散目录中高效灵活地测量宇宙大尺度结构统计量。Hermes在紧凑的缩放函数基中将目录重建为连续密度场，并用窗口滤波场之间的代数运算取代粒子元组的显式计数。通过选择窗口函数来表示标准的网格计数、两点及高阶相关函数的分箱方案，同时通过修改内核构建新统计量而无需重新设计估计器。我们引入了PyHermes，一个开源Python实现，结合了多分辨率重建、基于FFT的卷积、MPI/线程并行和GPU加速。它支持各向同性和各向异性两点统计、标记相关性、标准和多极三点函数、滤波统计以及导出物理场的微分算子。用宇宙学N体晕目录进行的测试展示了一系列聚类测量，并量化了该方法的计算效率和可扩展性。通过将场表示与统计窗口分离，单个重建场可用于许多标准和定制测量，使Hermes非常适合当前和未来星系巡天的大数据集。
英文摘要：
We present Hermes, an in situ multiresolution framework for efficient and flexible measurements of cosmic large-scale-structure statistics from discrete catalogues. Hermes reconstructs a catalogue as a continuous density field in a compact scaling-function basis and replaces explicit counting of particle tuples with algebraic operations among window-filtered fields. Standard binning schemes for counts-in-cells, two-point and higher-order correlation functions are thereby expressed through choices of window functions, while new statistics can be constructed by modifying the kernels without redesigning the estimator. We introduce PyHermes, an open-source Python implementation combining multiresolution reconstruction, FFT-based convolution, MPI/thread parallelism, and GPU acceleration. It supports isotropic and anisotropic two-point statistics, marked correlations, standard and multipole three-point functions, filtered statistics, and differential operators for derived physical fields. Tests with cosmological N-body halo catalogues demonstrate a range of clustering measurements and quantify the computational efficiency and scalability of the approach. By separating field representation from statistical windows, a single reconstructed field can be reused for many standard and customised measurements, making Hermes well suited to large data sets from current and future galaxy surveys.
