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arXiv · 2505.22709

Solving Milky Way-sized Systems with Haskap Pie: A Halo finding Algorithm with efficient Sampling, K-means clustering, tree-Assembly, Particle tracking, Python modules, Inter-code applicability, and Energy solving

Kirk S. S. Barrow·Thinh Huu Nguyen·Edward C. Skrabacz
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Abstract

We describe a new Python-based stand-alone halo finding algorithm, Haskap Pie, that combines several methods of halo finding and tracking into a single calculation. Our halo-finder flexibly solves halos for simulations produced by eight simulation codes (ART-I, ENZO, RAMSES, CHANGA, GADGET-3, GEAR, AREPO, and GIZMO) and for both zoom-in or full-box N-body or hydrodynamical simulations and includes a unified, robust set of pre-tuned parameters. When compared to Rockstar and Consistent Trees, our halo-finder tracks subhalos much longer and more consistently, produces halos with better constrained physical parameters, and returns a much denser halo mass function for halos with more than 100 particles. Our results also compare favorably to recently described specialized particle-tracking extensions to Rockstar. Haskap Pie is well-suited to a variety of studies of simulated galaxies and is particularly robust for a new generation of studies of merging and satellite galaxies. For our initial paper, we focus on describing our algorithm's ability to find and track halos and subhalos in complex Milky Way-sized halo systems.

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Kirk S. S. Barrow, Thinh Huu Nguyen, Edward C. Skrabacz. 2025-12-03. Solving Milky Way-sized Systems with Haskap Pie: A Halo finding Algorithm with efficient Sampling, K-means clustering, tree-Assembly, Particle tracking, Python modules, Inter-code applicability, and Energy solving. https://arxiv.org/abs/2505.22709

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