The public codes ASOHF and VORTEX for the post-processing of cosmological simulations David Vallés-Pérez 1,† Susana Planelles 1,2 Vicent Quilis 1,2 1Departament d'Astronomia i Astrofísica, Universitat de València 2Observatori Astronòmic, Universitat de València †david.valles-perez@uv.es ASOHF (Vallés-Pérez, Planelles & Quilis, 2022) ASOHF is an adaptive spherical overdensity DM halo finder and galaxy finder. Among its main features, we can count: Haloes are detected from density peaks in configuration space. Dynamical information is subsequently used through several complementary unbinding processes. Physically motivated, self-consistent definition of substructures (see below). Possibility to look for galaxies within DM haloes (see below). Complete merger trees, robust to the loss of haloes in a number of snapshots. Outputs include lists of haloes with several (∼50) properties and, optionally, complete member particle lists. Python packages provided. Optimal scaling properties, ∆twall ∼Npart/ncores in shared-memory platforms (OMP) and low memory footprint. Does not require external, special libraries to be installed. Parallelisation (beyond OMP) Large (≳50 Mpc) simulations with many (≳109) particles can benefit from the domain decompo- sition strategy, which is performed externally to the code. Different subdomains can be run con- currently or sequentially, and the different domain outputs are seamlessly merged afterwards. Substructure Substructures are delimited by their Jacobi radius, RJ, encompassing the region more bound to the satellite than to the host halo. This is usually a more restrictive definition than others based on the density profile alone. Galaxy finder Galaxies are looked for within DM (sub)haloes us- ing a variation of the SO method, and are finally characterised as fully independent objects (with different centre, bulk velocity, etc.). Galaxy ex- tents are delimited by density and density gradi- ent, in addition to phase space arguments. Learn more The paper The code The docs A&A 664 A42 github.com/dvallesp/ASOHF ASOHF.github.io 100 101 102 103 104 105 N( > M) 25 particles 100 particles ASOHF AHF ROCKSTAR SUBFIND Tinker+08 All haloes Subhaloes 101 102 103 104 105 106 Npart 109 1010 1011 1012 1013 1014 M (M ) 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 N( > M) / N( > M) vortex (Vallés-Pérez, Planelles & Quilis, 2021a,b; Vallés-Pérez et al. 2024) vortex is a post-processing code for performing several computationally-intensive analyses on the velocity fields of hydrodynamical simulations. While it was originally designed for block-based AMR simulations, it is now a general tool. Helmholtz-Hodge decomposition v(x) = vsol(x) | {z } Solenoidal part ∇·vsol=0 + vcomp(x) | {z } Compressive part ∇×vcomp=0 + vharm(x) | {z } Harmonic part (mostly irrelevant) The decomposition is straightforward for uniform grids, but much more complex for multi-resolution data, especially if O(N2) is to be avoided. We solve for a vector and a scalar potentials, such that: vcomp = −∇φ vsol = ∇× A ∇2φ = −∇· v ∇2A = −∇× v Elliptic PDEs can be addressed by a combination of FFT techniques (base grid) and iterative solvers (e.g., SOR), so that overall the cost is ≲O(N4/3). Multi-scale Reynolds decomposition v(x) = ⟨v⟩L(x)(x) | {z } Bulk velocity field + δv(x) | {z } Turbulent velocity field The bulk velocity is obtained by performing a top- hat filtering with position-dependent width, ⟨v⟩L(x)(x) = RRR |x′−x|<L(x) w(x′, |x −x′|)v(x′)d3x′ RRR |x′−x|<L(x) w(x′, |x −x′|)d3x′ where L(x) is determined by the smallest of: The scale required for δv(x) to converge, which is an indication of the outer scale of turbulence. The distance to the nearest influential (M ≳2 −3) shock. Learn more The papers The code CPC 263:107892 MNRAS 504(1):510 github.com/dvallesp/vortex Compressive and solenoidal part of the turbulent velocity field (same simulation as the other panels) vortex-p: Helmholtz-Hodge & Reynolds decomposition for particle-based and meshless data B