Galaxy formation and evolution

Abstract:

Using the two large cosmological hydrodynamical simulations, Horizon-AGN (H-AGN) and Horizon-noAGN (H-noAGN, no AGN feedback), we investigate how a typical sub-grid model for AGN feedback affects the evolution of the total density profiles (dark matter + stars) at the effective radius of massive early-type galaxies (M*>10^11 Msun). We have studied the dependencies of the mass-weighted density slope gamma'_tot with the effective radius, the galaxy mass and the host halo mass at z~0.3 and found that the inclusion of AGN feedbackalways leads to a much better agreement with observational values and trends. Our analysis suggests also that the inclusion of AGN feedback favours a strong correlation between gamma'_tot and the density slope of the dark matter component while, in the absence of AGN activity, gamma'_tot is rather strongly correlated with the density slope of the stellar component. Finally, we find that gamma'_tot derived from our samples of galaxies increases from z=2 to z=0,in good agreement with the expected observational trend. The derived slopes are slightly lower than in the data when AGN is included because the simulated galaxies tend to be too extended, especially the least massive ones. However, the simulated compact galaxies without AGN feedback have gamma'_tot values that are significantly too high compared to observations.

Abstract:

We review the concepts of the Voronoi binning technique (Cappellari & Copin 2003), which optimally solves the problem of preserving the maximum spatial resolution of general two-dimensional data, given a constraint on the minimum signal-to-noise ratio (S/N). This is achieved by partitioning the data in an adaptive fashion using a Voronoi tessellation with nearly hexagonal lattice. We review astrophysical applications of the method to X-ray data, integral-field spectroscopy, Fabry-Perot interferometry, N-body simulations, standard images and other regularly or irregularly sampled data. Voronoi binning, unlike adaptive smoothing, produces maps where the noise in the data can be visually assessed and spurious artifacts can be recognized. The method can be used to bin data according to any general criterion and not just S/N. It can be applied to higher dimensions and it can be used to generate optimal adaptive meshes for numerical simulations.

Abstract:

Dark Matter (DM) is generally assumed to be massive, cold and collisionless from the structure formation point of view. A more correct statement however is that DM indeed experiences collisional damping, but on a scale which is supposed to be too small to be relevant for structure formation. The aim of this paper is to present a Cold (although ``collisional'') Dark Matter particle whose matter power spectrum is damped and see whether it is distinguishable from standard candidates. To achieve this purpose, we calculate the collisional damping and free-streaming scales of neutralinos and non conventional candidates (say light particles heavier than ~1 MeV but lighter than O(10) GeV). The latter can be considered as Cold Dark Matter (CDM) particles in the sense that they become non relativistic before their thermal decoupling epoch. Unlike neutralinos, however, their linear matter power spectrum can be damped on scales of ~ 10^3 Msol due to their interactions. Since these scales are of cosmological interest for structure formation, we perform a series of numerical simulations to obtain the corresponding non linear matter power spectra P(k)_{nl} at the present epoch. We show that because of small scale regeneration, they all resemble each other at low redshifts, i.e. become very similar to a typical CDM matter power spectrum on all but the smallest scales. Therefore, even if lensing measurements at redshift below unity were to yield a P(k)_{nl} consistent with CDM models, this would not constitute a sufficiently robust evidence in favour of the neutralino to rule out alternative DM candidates.

Abstract:

Context. Accurate model predictions including the physics of baryons are required to make the most of the upcoming large cosmological surveys devoted to gravitational lensing. The advent of hydrodynamical cosmological simulations enables such predictions on sufficiently sizeable volumes. Aims. Lensing quantities (deflection, shear, convergence) and their statistics (convergence power spectrum, shear correlation functions, galaxy-galaxy lensing) are computed in the past lightcone built in the Horizon-AGN hydrodynamical cosmological simulation, which implements our best knowledge on baryonic physics at the galaxy scale in order to mimic galaxy populations over cosmic time. Methods. Lensing quantities are generated over a one square degree field of view by performing multiple-lens plane ray-tracing through the lightcone, taking full advantage of the 1 kpc resolution and splitting the line of sight over 500 planes all the way to redshift z~7. Two methods are explored (standard projection of particles with adaptive smoothing, and integration of the acceleration field) to assert a good implementation. The focus is on small scales where baryons matter most. Results. Standard cosmic shear statistics are impacted at the 10% level by the baryonic component for angular scales below a few arcmin. The galaxy-galaxy lensing signal, or galaxy-shear correlation function, is consistent with measurements for the redshift z~0.5 massive galaxy population. At higher redshift z>1, the impact of magnification bias on this correlation is relevant for separations greater than 1 Mpc. Conclusions. This work is pivotal for all current and upcoming weak lensing surveys and represents a first step towards building a full end-to-end generation of lensed mock images from large cosmological hydrodynamical simulations.

Abstract:

Accurate models of radiative cooling are a fundamental ingredient of modern cosmological simulations. Without cooling, accreted baryons will not efficiently dissipate their energy and collapse to the centres of haloes to form stars. It is well established that local variations in the amplitude and shape of the spectral energy distribution of the radiation field can drastically alter the cooling rate. Here we introduce deepCool, deepHeat, and deepMetal: methods for accurately modelling the total cooling rates, total heating rates, and metal-line only cooling rates of irradiated gas using artificial neural networks. We train our algorithm on a high-resolution cosmological radiation hydrodynamics simulation and demonstrate that we can predict the cooling rate, as measured with the photoionisation code CLOUDY, under the influence of a local radiation field, to an accuracy of ~5%. Our method is computationally and memory efficient, making it suitable for deployment in state-of-the-art radiation hydrodynamics simulations. We show that the circumgalactic medium and diffuse gas surrounding the central regions of a galaxy are most affected by the interplay of radiation and gas, and that standard cooling functions that ignore the local radiation field can incorrectly predict the cooling rate by more than an order of magnitude, indicating that the baryon cycle in galaxies is affected by the influence of a local radiation field on the cooling rate.