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

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 without the need for additional tuning or user-specified modeling 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. Our algorithm 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.