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Register a new userStatistics of Dark Matter Substructure: I. Model and Universal Fitting Functions
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Fangzhou Jiang
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We present a new, semi-analytical model describing the evolution of dark matter subhaloes. The model uses merger trees constructed using the method of Parkinson et al. (2008) to describe the masses and redshifts of subhaloes at accretion, which are subsequently evolved using a simple model for the orbit-averaged mass loss rates. The model is extremely fast, treats subhaloes of all orders, accounts for scatter in orbital properties and halo concentrations, and uses a simple recipe to convert subhalo mass to maximum circular velocity. The model accurately reproduces the average subhalo mass and velocity functions in numerical simulations. The inferred subhalo mass loss rates imply that an average dark matter subhalo loses in excess of 80 percent of its infall mass during its first radial orbit within the host halo. We demonstrate that the total mass fraction in subhaloes is tightly correlated with the `dynamical age of the host halo, defined as the number of halo dynamical times that have elapsed since its formation. Using this relation, we present universal fitting functions for the evolved and unevolved subhalo mass and velocity functions that are valid for any host halo mass, at any redshift, and for any {Lambda}CDM cosmology.
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We compare subhalo mass and velocity functions obtained from different simulations with different subhalo finders among each other, and with predictions from the new semi-analytical model of Jiang & van den Bosch (2014). We find that subhalo mass functions (SHMFs) obtained using different subhalo finders agree with each other at the level of ~ 20 percent, but only at the low mass end. At the massive end, subhalo finders that identify subhaloes based purely on density in configuration space dramatically underpredict the subhalo abundances by more than an order of magnitude. These problems are much less severe for subhalo velocity functions (SHVFs), indicating that they arise from issues related to assigning masses to the subhaloes, rather than from detecting them. Overall the predictions from the semi-analytical model are in excellent agreement with simulation results obtained using the more advanced subhalo finders that use information in six dimensional phase-space. In particular, the model accurately reproduces the slope and host-mass-dependent normalization of both the subhalo mass and velocity functions. We find that the SHMFs and SHVFs have power-law slopes of 0.82 and 2.6, respectively, significantly shallower than what has been claimed in several studies in the literature.
We present a study of unprecedented statistical power regarding the halo-to-halo variance of dark matter substructure. Using a combination of N-body simulations and a semi-analytical model, we investigate the variance in subhalo mass fractions and subhalo occupation numbers, with an emphasis on how these statistics scale with halo formation time. We demonstrate that the subhalo mass fraction, f_sub, is mainly a function of halo formation time, with earlier forming haloes having less substructure. At fixed formation redshift, the average f_sub is virtually independent of halo mass, and the mass dependence of f_sub is therefore mainly a manifestation of more massive haloes assembling later. We compare observational constraints on f_sub from gravitational lensing to our model predictions and simulation results. Although the inferred f_sub are substantially higher than the median LCDM predictions, they fall within the 95th percentile due to halo-to-halo variance. We show that while the halo occupation distribution of subhaloes, P(N|M), is super-Poissonian for large <N>, a well established result, it becomes sub-Poissonian for <N> < 2. Ignoring the non-Poissonity results in systematic errors of the clustering of galaxies of a few percent, and with a complicated scale- and luminosity-dependence. Earlier-formed haloes have P(N|M) closer to a Poisson distribution, suggesting that the dynamical evolution of subhaloes drives the statistics towards Poissonian. Contrary to a recent claim, the non-Poissonity of subhalo occupation statistics does not vanish by selecting haloes with fixed mass and fixed formation redshift. Finally, we use subhalo occupation statistics to put loose constraints on the mass and formation redshift of the Milky Way halo. Using observational constraints on the V_max of the most massive satellites, we infer that 0.25<M_vir/10^12M_sun/h<1.4 and 0.1<z_f<1.4 at 90% confidence.
Multicomponent dark matter with self-interactions, which allows for inter-
It is of great interest to measure the properties of substructures in dark matter halos at galactic and cluster scales. Here we suggest a method to constrain substructure properties using the variance of weak gravitational flexion in a galaxy-galaxy lensing context. We show the effectiveness of flexion variance in measuring substructures in N-body simulations of dark matter halos, and present the expected galaxy-galaxy lensing signals. We show the insensitivity of the method to the overall galaxy halo mass, and predict the methods signal-to-noise for a space-based all-sky survey, showing that the presence of substructure down to 10^9 M_odot halos can be reliably detected.
We study the application of machine learning techniques for the detection of the astrometric signature of dark matter substructure. In this proof of principle a population of dark matter subhalos in the Milky Way will act as lenses for sources of extragalactic origin such as quasars. We train ResNet-18, a state-of-the-art convolutional neural network to classify angular velocity maps of a population of quasars into lensed and no lensed classes. We show that an SKA -like survey with extended operational baseline can be used to probe the substructure content of the Milky Way.
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