Related papers: Solving Milky Way-sized Systems with Haskap Pie: A…
We introduce a halo solving and tracking procedure that intrinsically treats dark matter halos as non-spherical objects by leveraging the bound particle searching techniques used in Haskap Pie. The AGORA Collaboration's hydrodynamic…
Modern N-body cosmological simulations contain billions ($10^9$) of dark matter particles. These simulations require hundreds to thousands of gigabytes of memory, and employ hundreds to tens of thousands of processing cores on many compute…
We present a new algorithm for generating merger trees and halo catalogs which explicitly ensures consistency of halo properties (mass, position, and velocity) across timesteps. Our algorithm has demonstrated the ability to improve both the…
We have developed a new halo finding method, Physically Self-Bound (PSB) group finding algorithm, which can efficiently identify halos located even at crowded regions. This method combines two physical criteria such as the tidal radius of a…
We present a new algorithm for identifying dark matter halos, substructure, and tidal features. The approach is based on adaptive hierarchical refinement of friends-of-friends groups in six phase-space dimensions and one time dimension,…
We describe a new method (\textsc{CompaSO}) for identifying groups of particles in cosmological $N$-body simulations. \textsc{CompaSO} builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius…
Over $90$\% of dark matter haloes in cosmological simulations have unresolved properties. This can hinder the dynamical range of simulations and result in systematic biases when modelling cosmological tracers. We aim to more precisely…
Context. New-generation cosmological simulations are providing huge amounts of data, whose analysis becomes itself a cutting-edge computational problem. In particular, the identification of gravitationally bound structures, known as halo…
We present VELOCIraptor, a massively parallel galaxy/(sub)halo finder that is also capable of robustly identifying tidally disrupted objects and separate stellar halos from galaxies. The code is written in c++11, use the MPI and OpenMP…
In this study, we perform a halo-finder code comparison between Rockstar and CompaSO. Based on our previous analysis aiming at quantifying resolution of $N$-body simulations by exploiting large (up to $N=4096^3$) simulations of scale-free…
[abridged] We present a detailed comparison of fundamental dark matter halo properties retrieved by a substantial number of different halo finders. These codes span a wide range of techniques including friends-of-friends (FOF),…
Cosmological N-body simulations rank among the most computationally intensive efforts today. A key challenge is the analysis of structure, substructure, and the merger history for many billions of compact particle clusters, called halos.…
Identifying galaxies in hydrodynamical simulations is a difficult task, particularly in regions of high density such as galaxy groups and clusters. We present a new scale-free shape-independent algorithm to robustly and accurately identify…
We present a new technique that allows us to compute ensemble statistics on a local basis, directly relating halo properties to their local environment. This is achieved by the use of a correlated ensemble in which the Large Scale Structure…
Self-interacting dark matter provides a promising alternative for the cold dark matter paradigm to solve potential small-scale galaxy formation problems. Nearly all self-interacting dark matter simulations so far have considered only…
We present and test a new halo finder based on the spherical overdensity (SO) method. This new adaptive spherical overdensity halo finder (ASOHF) is able to identify dark matter haloes and their substructures (subhaloes) down to the scales…
Dark matter subhaloes are key for the predictions of simulations of structure formation, but their existence frequently ends prematurely due to two technical issues, namely numerical disruption in N-body simulations and halo finders failing…
We build upon Ordering Points To Identify Clustering Structure (OPTICS), a hierarchical clustering algorithm well-known to be a robust data-miner, in order to produce Halo-OPTICS, an algorithm designed for the automatic detection and…
We describe a new algorithm for finding substructures within dark matter haloes from N-body simulations. The algorithm relies upon the fact that dynamically distinct substructures in a halo will have a {\em local} velocity distribution that…
We present a detailed comparison of the substructure properties of a single Milky Way sized dark matter halo from the Aquarius suite at five different resolutions, as identified by a variety of different (sub-)halo finders for simulations…