THE ROCKSTAR PHASE-SPACE TEMPORAL HALO FINDER AND THE VELOCITY OFFSETS OF CLUSTER CORES

By P. Behroozi, Risa Wechsler, Hao-Yi Wu. 2011

898 citations recorded by the source corpus.

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, which allows for robust (grid-independent, shape-independent, and noise-resilient) tracking of substructure; as such, it is named rockstar (Robust Overdensity Calculation using K-Space Topologically Adaptive Refinement). Our method is massively parallel (up to 105 CPUs) and runs on the largest current simulations (>1010 particles) with high efficiency (10 CPU hours and 60 gigabytes of memory required per billion particles analyzed). A previous paper has shown rockstar to have excellent recovery of halo properties; we expand on these comparisons with more tests and higher-resolution simulations. We show a significant improvement in substructure recovery compared to several other halo finders and discuss the theoretical and practical limits of simulations in this regard. Finally, we present results that demonstrate conclusively that dark matter halo cores are not at rest relative to the halo bulk or substructure average velocities and have coherent velocity offsets across a wide range of halo masses and redshifts. For massive clusters, these offsets can be up to 350 km s−1 at z = 0 and even higher at high redshifts. Our implementation is publicly available at http://code.google.com/p/rockstar.

Subfields: Astronomy and Astrophysics, Instrumentation, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/4d338d1f1b945307eb38dd61d4e37f23bbbd0ac7

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Citations in the corpus

DEPENDENCE OF THE OUTER DENSITY PROFILES OF HALOS ON THEIR MASS ACCRETION RATE (2014, methodology): “Halo Samples and Resolution Limits We used the phase space–based halo finder Rockstar (Behroozi et al. 2013a) to extract all isolated halos and subhalos from the 100 snapshots of each simulation, and applied the merger tree code of Behroozi et al. (2013b) to the halo catalogs.”
Weak Lensing Measurement of the Mass--Richness Relation of SDSS redMaPPer Clusters (2016, methodology): “We use the halo catalogue generated by the Rockstar halo finder (Behroozi et al. 2013).”
The first-year shear catalog of the Subaru Hyper Suprime-Cam Subaru Strategic Program Survey (2017, methodology): “To identify halos in each N-body simulation output, we used the software Rockstar (Behroozi et al. 2013) that identifies halos from the clustering of N-body particles in phase space.”
The upper bound on the lowest mass halo (2016, methodology): “We calculate the expected distribution of stellar masses of an Mvir = 10 11M halo assuming the SMHM relations of Behroozi et al. (2013a) and Moster et al. (2013).”
Too big to fail in the Local Group (2014, methodology): “We additionally select haloes that are expected to have formed stars based upon Vpeak, which is defined as Vmax of the main branch of the halo’s merger tree, built with CONSISTENT TREES (Behroozi et al. 2013b), at the timestep when the halo reaches its maximal mass (see GK14 for more details).”
Cosmological parameters from the comparison of peculiar velocities with predictions from the 2M++ density field (2014, methodology): “From the particle positions and velocities of the simulation a halo catalogue was formed using ROCKSTAR (Behroozi et al. 2013) consisting of 693948 halos between 5.5 × 1011 and 2.2 × 1015 h−1 M (between 8 and 31809 particles).”
RECONCILING DWARF GALAXIES WITH ΛCDM COSMOLOGY: SIMULATING A REALISTIC POPULATION OF SATELLITES AROUND A MILKY WAY–MASS GALAXY (2016, methodology): “To identify (sub)halos and their galaxies, we use a modified version of the six-dimensional phase-space halo finder rockstar13 (Behroozi et al. 2013), which accounts for multiple species and assigns dark-matter, gas, and star particles to (sub)halos.”
Characterization of subhalo structural properties and implications for dark matter annihilation signals (2016, background): “…different algorithms have been proposed in order to identify small structures within simulated host halos (Ghigna et al. 2000; Weller et al. 2005; Shaw et al. 2006; Springel et al. 2001; Gill et al. 004a,b; Tormen & Moscardini 2004; Giocoli et al. 008b, 2010; Han et al. 2012; Behroozi et al. 2013).”
Sweating the small stuff: simulating dwarf galaxies, ultra-faint dwarf galaxies, and their own tiny satellites (2015, methodology): “This abundance matching relation, obtained by updating the Behroozi et al. (2013a) relation with a faint-end slope derived from the measurements of the GAMA survey (Baldry et al. 2012), accurately reproduces the stellar mass functions of the Milky Way and Andromeda satellites, as well as the…”
The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling the clustering and halo occupation distribution of BOSS CMASS galaxies in the Final Data Release (2015, methodology): “The merger trees algorithm (Behroozi et al. 2013b) was used to estimate the peak circular velocity over the history of the halo, Vpeak, which we use to perform the abundance matching.”
Milky Way Satellite Census. II. Galaxy–Halo Connection Constraints Including the Impact of the Large Magellanic Cloud (2019, methodology): “Halo catalogs and merger trees were generated using the ROCKSTAR halo finder and the CONSISTENT-TREES merger code (Behroozi et al. 2013b,c).”
General relativity and cosmic structure formation (2015, methodology): “We generate [18] two halo catalogs containing ∼ 500,000 halos each, one using our relativistic approach and one with the traditional Newtonian approach as reference, starting from identical linear initial conditions.”
Disruption of Dark Matter Substructure: Fact or Fiction? (2017, methodology): “…the popular technique of subhalo abundance matching (e.g., Vale & Ostriker 2004; Conroy et al. 2006; Guo et al. 2010; Hearin et al. 2013; Moster et al. 2013; Behroozi et al. 2013b), in which galaxies are assigned to dark matter host and subhaloes in simulations to create mock galaxy samples.”
Halo and subhalo demographics with Planck cosmological parameters: Bolshoi–Planck and MultiDark–Planck simulations (2016, methodology): “In this paper we use the Rockstar halo finder (Behroozi, Wechsler & Wu 2013) and Consistent Trees (Behroozi et al. 2013) to analyze results for the recent Bolshoi-Planck (BolshoiP), Small MultiDark-Planck (SMDPL) and MultiDark-Planck (MDPL) simulations based on the 2013 Planck cosmological…”
gevolution: a cosmological N-body code based on General Relativity (2016, methodology): “Figure 3 shows the comparison of the halo mass distributions of DM halos at redshift z = 0, extracted for the simulations using the Rockstar halo finder [45].”
A 2.5 per cent measurement of the growth rate from small-scale redshift space clustering of SDSS-III CMASS galaxies (2014, background): “Therefore, the amplitude of the bulk velocities in this work seems consistent with that found in Behroozi et al. (2013) using the ROCKSTAR halo finder with slightly higher mass resolution.”
Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters (2018, methodology): “Halos were defined using a spherical overdensity mass definition of 200 times the background density and were identified with the ROCKSTAR halo finder (Behroozi et al. 2013).”
THE RELATION BETWEEN STAR FORMATION RATE AND STELLAR MASS FOR GALAXIES AT 3.5 ⩽ z ⩽ 6.5 IN CANDELS (2014, methodology): “Here, we use the parameterization of Behroozi et al. (2013a), who provide simple functions to track the number density evolution of the progenitors of galaxies.”