No Arabic abstract
Hierarchical structure formation implies that the number of subhalos within a dark matter halo depends not only on halo mass, but also on the formation history of the halo. This dependence on the formation history, which is highly correlated with halo concentration, can account for the super-Poissonian scatter in subhalo occupation at a fixed halo mass that has been previously measured in simulations. Here we propose a model to predict the subhalo abundance function for individual host halos, that incorporates both halo mass and concentration. We combine results of cosmological simulations with a new suite of zoom-in simulations of Milky Way-mass halos to calibrate our model. We show the model can successfully reproduce the mean and the scatter of subhalo occupation in these simulations. The implications of this correlation between subhalo abundance and halo concentration are further investigated. We also discuss cases in which inferences about halo properties can be affected if this correlation between subhalo abundance and halo concentration is ignored; in these cases our model would give a more accurate inference. We propose that with future deep surveys, satellite occupation in the low-mass regime can be used to verify the existence of halo assembly bias.
Empirical methods for connecting galaxies to their dark matter halos have become essential for interpreting measurements of the spatial statistics of galaxies. In this work, we present a novel approach for parameterizing the degree of concentration dependence in the abundance matching method. This new parameterization provides a smooth interpolation between two commonly used matching proxies: the peak halo mass and the peak halo maximal circular velocity. This parameterization controls the amount of dependence of galaxy luminosity on halo concentration at a fixed halo mass. Effectively this interpolation scheme enables abundance matching models to have adjustable assembly bias in the resulting galaxy catalogs. With the new 400 Mpc/h DarkSky Simulation, whose larger volume provides lower sample variance, we further show that low-redshift two-point clustering and satellite fraction measurements from SDSS can already provide a joint constraint on this concentration dependence and the scatter within the abundance matching framework.
Understanding the impact of environment on the formation and evolution of dark matter halos and galaxies is a crucial open problem. Studying statistical correlations in large simulated populations sheds some light on these impacts, but the causal effect of an environment on individual objects is harder to pinpoint. Addressing this, we present a new method for resimulating a single dark matter halo in multiple large-scale environments. In the initial conditions, we splice (i.e. insert) the Lagrangian region of a halo into different Gaussian random fields, while enforcing consistency with the statistical properties of $Lambda$CDM. Applying this technique, we demonstrate that the mass of halos is primarily determined by the density structure inside their Lagrangian patches, while the halos concentration is more strongly affected by environment. The splicing approach will also allow us to study, for example, the impact of the cosmic web on accretion processes and galaxy quenching.
Halo bias is the main link between the matter distribution and dark matter halos. In its simplest form, halo bias is determined by halo mass, but there are known additional dependencies on other halo properties which are of consequence for accurate modeling of galaxy clustering. Here we present the most precise measurement of these secondary-bias dependencies on halo age, concentration, and spin, for a wide range of halo masses spanning from 10$^{10.7}$ to 10$^{14.7}$ $h^{-1}$ M$_{odot}$. At the high-mass end, we find no strong evidence of assembly bias for masses above M$_{vir}$ $sim10^{14}$ $h^{-1}$ M$_{odot}$. Secondary bias exists, however, for halo concentration and spin, up to cluster-size halos, in agreement with previous findings. For halo spin, we report, for the first time, two different regimes: above M$_{vir}sim$10$^{11.5}$ $h^{-1}$ M$_{odot}$, halos with larger values of spin have larger bias, at fixed mass, with the effect reaching almost a factor 2. This trend reverses below this characteristic mass. In addition to these results, we test, for the first time, the performance of a multi-tracer method for the determination of the relative bias between different subsets of halos. We show that this method increases significantly the signal-to-noise of the secondary-bias measurement as compared to a traditional approach. This analysis serves as the basis for follow-up applications of our multi-tracer method to real data.
We present the measurements of the luminosity-dependent redshift-space three-point correlation functions (3PCFs) for the Sloan Digital Sky Survey (SDSS) DR7 Main galaxy sample. We compare the 3PCF measurements to the predictions from three different halo and subhalo models. One is the halo occupation distribution (HOD) model and the other two are extensions of the subhalo abundance matching (SHAM) model by allowing the central and satellite galaxies to have different occupation distributions in the host halos and subhalos. Parameters in all the models are chosen to best describe the projected and redshift-space two-point correlation functions (2PCFs) of the same set of galaxies. All three model predictions agree well with the 3PCF measurements for the most luminous galaxy sample, while the HOD model better performs in matching the 3PCFs of fainter samples (with luminosity threshold below $L^*$), which is similar in trend to the case of fitting the 2PCFs. The decomposition of the model 3PCFs into contributions from different types of galaxy triplets shows that on small scales the dependence of the 3PCFs on triangle shape is driven by nonlinear redshift-space distortion (and not by the intrinsic halo shape) while on large scales it reflects the filamentary structure. The decomposition also reveals more detailed differences in the three models, which are related to the radial distribution, the mean occupation function, and the velocity distribution of satellite galaxies inside halos. The results suggest that galaxy 3PCFs can further help constrain the above galaxy-halo relation and test theoretical models.
The simplest analyses of halo bias assume that halo mass alone determines halo clustering. However, if the large scale environment is fixed, then halo clustering is almost entirely determined by environment, and is almost completely independent of halo mass. We show why. Our analysis is useful for studies which use the environmental dependence of clustering to constrain cosmological and galaxy formation models. It also shows why many correlations between galaxy properties and environment are merely consequences of the underlying correlations between halos and their environments, and provides a framework for quantifying such inherited correlations.