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Register a new userBaryon effects on the internal structure of LCDM halos in the EAGLE simulations
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Matthieu Schaller
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We investigate the internal structure and density profiles of halos of mass $10^{10}-10^{14}~M_odot$ in the Evolution and Assembly of Galaxies and their Environment (EAGLE) simulations. These follow the formation of galaxies in a $Lambda$CDM Universe and include a treatment of the baryon physics thought to be relevant. The EAGLE simulations reproduce the observed present-day galaxy stellar mass function, as well as many other properties of the galaxy population as a function of time. We find significant differences between the masses of halos in the EAGLE simulations and in simulations that follow only the dark matter component. Nevertheless, halos are well described by the Navarro-Frenk-White (NFW) density profile at radii larger than ~5% of the virial radius but, closer to the centre, the presence of stars can produce cuspier profiles. Central enhancements in the total mass profile are most important in halos of mass $10^{12}-10^{13}M_odot$, where the stellar fraction peaks. Over the radial range where they are well resolved, the resulting galaxy rotation curves are in very good agreement with observational data for galaxies with stellar mass $M_*<5times10^{10}M_odot$. We present an empirical fitting function that describes the total mass profiles and show that its parameters are strongly correlated with halo mass.
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We report results for the alignments of galaxies in the EAGLE and cosmo-OWLS simulations as a function of galaxy separation and halo mass. The combination of these hydro-cosmological simulations enables us to span four orders of magnitude in halo mass ($10.7<log_{10}(M_{200}/[h^{-1}M_odot])<15$) and a large range of separations ($-1<log_{10}(r/[h^{-1}Mpc])< 2$). We focus on two classes of alignments: the orientations of galaxies with respect to either the directions to, or the orientations of, surrounding galaxies. We find that the strength of the alignment is a strongly decreasing function of the distance between galaxies. The orientation-direction alignment can remain significant up to ~100 Mpc, for galaxies hosted by the most massive haloes in our simulations. Galaxies hosted by more massive subhaloes show stronger alignment. At a fixed halo mass, more aspherical or prolate galaxies exhibit stronger alignments. The spatial distribution of satellites is anisotropic and significantly aligned with the major axis of the main host halo. The major axis of satellite galaxies, when all stars are considered, are preferentially aligned towards the centre of the main host halo. The predicted projected direction-orientation alignment, $epsilon_{g+}(r_{p})$, is in broad agreement with recent observations when only stars within the typical observable extent of a galaxy are used to define galaxy orientations. We find that the orientation-orientation alignment is weaker than the orientation-direction alignment on all scales. Overall, the strength of galaxy alignments depends strongly on the subset of stars that are used to measure the orientations of galaxies and it is always weaker than the alignment of the dark matter haloes. Thus, alignment models that use halo orientation as a direct proxy for galaxy orientation will overestimate the impact of intrinsic alignments on weak lensing analyses.
The abundance and distribution of metals in galaxy clusters contains valuable information about their chemical history and evolution. By looking at how metallicity evolves with redshift, it is possible to constrain the different metal production channels. We use the C-EAGLE clusters, a sample of 30 high resolution ($m_{gas} simeq 1.8times 10^{6}$ M$_{odot}$) cluster zoom simulations, to investigate the redshift evolution of metallicity, with particular focus on the cluster outskirts. The early enrichment model, in which the majority of metals are produced in the core of cluster progenitors at high redshift, suggests that metals in cluster outskirts have not significantly evolved since $z=2$. With the C-EAGLE sample, we find reasonable agreement with the early enrichment model as there is very little scatter in the metallicity abundance at large radius across the whole sample, out to at least $z=2$. The exception is Fe for which the radial dependence of metallicity was found to evolve at low redshift as a result of being mainly produced by Type Ia supernovae, which are more likely to be formed at later times than core-collapse supernovae. We also found considerable redshift evolution of metal abundances in the cores of the C-EAGLE clusters which has not been seen in other simulations or observation based metallicity studies. Since we find this evolution to be driven by accretion of low metallicity gas, it suggests that the interaction between outflowing, AGN heated material and the surrounding gas is important for determining the core abundances in clusters.
Dissipative dark matter self-interactions can affect halo evolution and change its structure. We perform a series of controlled N-body simulations to study impacts of the dissipative interactions on halo properties. The interplay between gravitational contraction and collisional dissipation can significantly speed up the onset of gravothermal collapse, resulting in a steep inner density profile. For reasonable choices of model parameters controlling the dissipation, the collapse timescale can be a factor of 10-100 shorter than that predicted in purely elastic self-interacting dark matter. The effect is maximized when energy loss per collision is comparable to characteristic kinetic energy of dark matter particles in the halo. Our simulations provide guidance for testing the dissipative nature of dark matter with astrophysical observations.
We study the effects of droplet finite size on the structure of nanogel particles synthesized by random crosslinking of molecular polymers diluted in nanoemulsions. For this, we use a bead-spring computer model of polymer-like structures that mimics the confined random crosslinking process corresponding to irradiation- or electrochemically-induced crosslinking methods. Our results indicate that random crosslinking under strong confinement can lead to unusual nanogel internal structures, with a central region less dense than the external one, whereas under moderate confinement the resulting structure has a denser central region. We analyze the topology of the polymer networks forming nanogel particles with both types of architectures, their overall structural parameters, their response to the quality of the solvent and compare the cases of non-ionic and ionic systems.
High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure clustering statistics of the large scale structure. Typically, zoom simulations of individual regions are used to study rare environments, and semi-analytic models and halo occupation models applied to dark matter only (DMO) simulations are used to study the Universe in the large-volume regime. We propose a new approach, using a machine learning framework to explore the halo-galaxy relationship in the periodic EAGLE simulations, and zoom C-EAGLE simulations of galaxy clusters. We train a tree based machine learning method to predict the baryonic properties of galaxies based on their host dark matter halo properties. The trained model successfully reproduces a number of key distribution functions for an infinitesimal fraction of the computational cost of a full hydrodynamic simulation. By training on both periodic simulations as well as zooms of overdense environments, we learn the bias of galaxy evolution in differing environments. This allows us to apply the trained model to a larger DMO volume than would be possible if we only trained on a periodic simulation. We demonstrate this application using the $(800 ; mathrm{Mpc})^3$ P-Millennium simulation, and present predictions for key baryonic distribution functions and clustering statistics from the EAGLE model in this large volume.
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