Substructures in hydrodynamical cluster simulations

By Klaus Dolag, Stefano Borgani, Giuseppe Murante, Volker Springel. 2008

675 citations recorded by the source corpus.

The abundance and structure of dark matter subhaloes have been analysed extensively in recent studies of dark-matter-only simulations, but comparatively little is known about the impact of baryonic physics on halo substructures. We here extend the SUBFIND algorithm for substructure identification such that it can be reliably applied to dissipative hydrodynamical simulations that include star formation. This allows, in particular, the identification of galaxies as substructures in simulations of clusters of galaxies and a determination of their content of gravitationally bound stars, dark matter and hot and cold gas. Using a large set of cosmological cluster simulations, we present a detailed analysis of halo substructures in hydrodynamical simulations of galaxy clusters, focusing in particular on the influence both of radiative and non-radiative gas physics and of non-standard physics such as thermal conduction and feedback by galactic outflows. We also examine the impact of numerical nuisance parameters such as artificial viscosity parameterizations. We find that diffuse hot gas is efficiently stripped from subhaloes when they enter the highly pressurized cluster atmosphere. This has the effect of decreasing the subhalo mass function relative to a corresponding dark-matter-only simulation. These effects are mitigated in radiative runs, where baryons condense in the central subhalo regions and form compact stellar cores. However, in all cases, only a very small fraction, of the order of one per cent, of subhaloes within the cluster virial radii preserve a gravitationally bound hot gaseous atmosphere. The fraction of mass contributed by gas in subhaloes is found to increase with the cluster-centric distance. Interestingly, this trend extends well beyond the virial radii, thus showing that galaxies feel the environment of the pressurized cluster gas over fairly large distances. The compact stellar cores (i.e. galaxies) are generally more resistant against tidal disruption than pure dark matter subhaloes. Still, the fraction of star-dominated substructures within our simulated clusters is only ~10 per cent. We expect that the finite resolution in our simulations makes the galaxies overly susceptible to tidal disruption, hence the above fraction of star-dominated galaxies should represent a lower limit for the actual fraction of galaxies surviving the disruption of their host dark matter subhalo.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/20f2920802b60f4716271087cde781879e8d0d70

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

In Instrumentation

Citations in the corpus

Galaxy morphology and star formation in the Illustris Simulation at z = 0 (2015, methodology): “…(Springel et al. 2001), which uses a friends-of-friends algorithm (e.g., Davis et al. 1985) with a linking length of 0.2 times the mean particle separation to identify bound dark matter halos, and then applies a secondary linking stage to associate baryonic mass to these halos (Dolag et al. 2009).”
The unexpected diversity of dwarf galaxy rotation curves (2015, methodology): “Structures are identified in our simulations using the SUBFIND algorithm (Springel et al. 2001; Dolag et al. 2009).”
SUBSTRUCTURE DEPLETION IN THE MILKY WAY HALO BY THE DISK (2009, background): “However, these estimates of substructure abundance at different locations within a halo, from the solar neighborhoodto the outskirts of the Milky Way, are based on simulations that include only dark matter and do not account for ordinary baryonic material (but see Dolag et al. 2008).”
The EAGLE simulations of galaxy formation: the importance of the hydrodynamics scheme (2015, methodology): “Haloes were identified using the Friends-of-Friends (FoF) algorithm (Davis et al. 1985) with linking length 0.2 times the mean interparticle distance, and bound structures within them were then identified using the SUBFIND code (Springel et al. 2001; Dolag et al. 2009).”
Bimodality of low-redshift circumgalactic O VI in non-equilibrium EAGLE zoom simulations (2016, methodology): “We run the SUBFIND algorithm (Springel et al. 2001; Dolag et al. 2009) on our zoom outputs, and quantify halo mass, M200, as the mass within a sphere with mean enclosed density 200× the critical overdensity centred on the galaxy’s potential minimum.”
CLASH-VLT: The mass, velocity-anisotropy, and pseudo-phase-space density profiles of the z = 0.44 galaxy cluster MACS J1206.2-0847 (2013, background): “Comparing different mass profile determinations can therefore help assessing the contribution of non-thermal pressure to the ICM and the elongation along the line-of-sight (e.g. Morandi & Limousin 2012; Sereno et al. 2012).”
X-ray coronae in simulations of disc galaxy formation (2010, methodology): “As decribed in C09, we identify bound haloes using the SUBFIND algorithm presented by Dolag et al. (2009), which extends the standard implementation (Springel et al. 2001) by also considering baryonic particles when identifying self-bound substructures.”
The BAHAMAS project: Calibrated hydrodynamical simulations for large-scale structure cosmology (2016, positive): “Artificial haloes are removed by performing an unbinding calculation with the subfind algorithm (Springel et al. 2001; Dolag et al. 2009): any FoF halo that does not have at least one self-bound substructure (a ‘subhalo’) is removed from the FoF groups list.”
Disruption of Dark Matter Substructure: Fact or Fiction? (2017, background): “…literature on how baryons may impact the abundance and demographics of dark matter substructure (e.g., Macciò et al. 2006; Weinberg et al. 2008; Dolag et al. 2009; Arraki et al. 2014; Brooks & Zolotov 2014; Despali & Vegetti 2016; Fiacconi et al. 2016; Wetzel et al. 2016; Garrison-Kimmel et…”
Shaping the galaxy stellar mass function with supernova- and AGN-driven winds (2012, methodology): “In our simulations, we find galaxies and their stellar masses by applying the Subfind substructure finder (Springel et al. 2001; Dolag et al. 2009) to FoF groups identified with a linking length of 0.2 times the mean interparticle spacing.”
The relation between velocity dispersion and mass in simulated clusters of galaxies: dependence on the tracer and the baryonic physics (2013, methodology): “The SUBFIND algorithm (Springel et al. 2001; Dolag et al. 2009) is used to identify the main halo, whose centre corresponds to the position of the most bound DM particle, and substructures within each FoF group.”
The UV continua and inferred stellar populations of galaxies at z ~7-9 revealed by the Hubble Ultra-Deep Field 2012 campaign (2012, methodology): “Substructures are identified by using the SUBFIND algorithm (Springel, Yoshida & White 2001; Dolag et al. 2009) which discriminates between bound and nonbound particles.”
Towards a realistic population of simulated galaxy groups and clusters (2013, methodology): “Artificial haloes are removed by performing an unbinding calculation with the subfind algorithm (Springel et al. 2001; Dolag et al. 2009): any FoF halo that does not have at least one self-bound substructure (called subhalo) is removed from the FoF groups list.”
Bent by baryons: the low-mass galaxy-halo relation (2014, methodology): “We use a Friends-of-Friends algorithm (FoF, e.g. Davis et al. 1985) to identify overdense structures (FoF-groups), and the subfind algorithm (Springel et al. 2001; Dolag et al. 2009) to identify self-bound substructures within them.”
First results from the IllustrisTNG simulations: the stellar mass content of groups and clusters of galaxies (2017, methodology): “Haloes, subhaloes, and their basic properties are obtained with the FOF and SUBFIND algorithms (Davis et al. 1985; Springel et al. 2001; Dolag et al. 2009), at each of 100 snapshots saved from z ∼ 20 to z = 0.”
The formation of massive, compact galaxies at z = 2 in the Illustris simulation (2014, methodology): “Gravitationally-bound substructures of baryons and dark matter (‘subhalos’) are identified using the SUBFIND algorithm (Springel et al. 2001; Dolag et al. 2009).”
Colours and luminosities of z = 0.1 galaxies in the eagle simulation (2015, methodology): “We then use the SUBFIND algorithm (Springel et al. 2001; Dolag et al. 2009) to identify self-bound substructures (subhaloes) within haloes of dark matter, stars and gas, which we identify with galaxies.”
The rates and modes of gas accretion on to galaxies and their gaseous haloes (2010, methodology): “We use SUBFIND (Springel et al. 2001; Dolag et al. 2009) on the FoF output to find the gravitationally bound particles and to identify subhaloes.”
A case study of hurdle and generalized additive models in astronomy: the escape of ionizing radiation (2018, methodology): “Haloes were identified with a SUBFIND algorithm (e.g. Dolag et al. 2009), and their redshift evolution was followed down to z = 6 using a merger tree approach (Neistein et al. 2012).”
Simulating galaxy formation with the IllustrisTNG model (2017, methodology): “These include the SUBFIND and FOF algorithms to identify haloes and subhaloes (Springel et al. 2005; Dolag et al. 2009); a custom output strategy, whereby particle information is ordered on disk according to its hierarchical group membership, allowing for rapid retrieval and query; a Monte Carlo…”