Solving Milky Way–sized Systems with H <scp>askap</scp> P <scp>ie</scp> : A Halo Finding Algorithm with Efficient Sampling, K-means Clustering, Tree-assembly, Particle Tracking, Python Modules, Intercode 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 zoomed-in or full-box N-body or hydrodynamical simulations and includes a unified, robust set of pretuned parameters. When compared to Rockstar ( P. S. Behroozi et al.) and Consistent Trees , our halo-finder tracks subhalos for 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.