中文

RAYTHEIA: A high-performance ray-tracing algorithm for three-dimensional direction-dependent equations in astronomical simulations

天体物理仪器与方法 2026-05-12 v1

摘要

We present RAYTHEIA, a high-performance reverse ray-tracing algorithm designed to efficiently solve three-dimensional direction-dependent equations in astronomical simulations. The algorithm uses a dual-grid framework in which the native simulation mesh -- serving as the source grid for ray emission -- and an adaptive mesh refinement (AMR) Cartesian contribution grid are constructed for efficient ray-walking and contribution accumulation. The core of the algorithm integrates a leaf-only linear-octree data structure to reduce memory overhead, the digital differential analyzer (DDA) traversal method to efficiently determine the ray-walking path, Morton Code indexing to fast leaf cell lookup during traversal, and the slab method to analytically compute the path length. Furthermore, RAYTHEIA employs a hybrid (MPI/OpenMP) distributed parallel framework with a chunk-to-chunk communication strategy, achieving exceptional, near-ideal linear speed-up ratio and delivering high-end performance. We integrate RAYTHEIA with the 3D-PDR code to solve the complex chemistry and radiation transfer in photodissociation regions (PDRs). This allowed the modelling of three-dimensional PDR chemistry in a turbulent, star-forming cloud at an unprecedented resolution of 5123512^3 grid cells. The algorithm demonstrates accuracy and convergence even at low angular resolutions. We further showcase the capabilities of RAYTHEIA by producing high-resolution synthetic emission maps of key diagnostic lines of a star-forming region capturing physical effects such as [O I] 63μ63\mum self-absorption, measuring the [C I]-bright but CO-dark molecular gas, and deriving a CO-to-H2_2 conversion factor in agreement with observations.

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引用

@article{arxiv.2605.09882,
  title  = {RAYTHEIA: A high-performance ray-tracing algorithm for three-dimensional direction-dependent equations in astronomical simulations},
  author = {Zhengping Zhu and Thomas G. Bisbas and Xuefei Tang and Brandt A. L. Gaches and Tianwei Zhang and Huaxi Chen},
  journal= {arXiv preprint arXiv:2605.09882},
  year   = {2026}
}

备注

15 pages, 13 figures. MNRAS accepted