Date of Award

Fall 10-1-2021

Document Type


Degree Name

Doctor of Philosophy (PhD)



First Advisor

van den Bosch, Frank


Accurately predicting the abundance and structural evolution of dark matter subhaloes is crucial for understanding galaxy formation, large-scale structure, and constraining the nature of dark matter. Due to the nonlinear nature of subhalo evolution, cosmological N-body simulations remain its primary method of investigation. Subhaloes in such simulations have recently been shown to still be heavily impacted by artificial disruption, diminishing the information content (at small scales) of the simulations and all derivative semi-analytical models calibrated against them. A model of the evolved subhalo density structure: Our recent release of the DASH library of high-resolution, idealized N-body simulations of the tidal evolution of subhaloes (unhindered by numerical over-merging due to discreteness noise or force softening) enables a more accurate calibration of semi-analytical treatments of dark matter substructure evolution. We use DASH to calibrate a highly accurate, simply parametrized empirical model of the evolved subhalo density profile (ESHDP), which captures the impact of tidal heating and stripping. By testing previous findings that the structural evolution of a tidally truncated subhalo depends solely on the fraction of mass stripped, independent of the details of the stripping, we identify an additional dependence on the initial subhalo concentration. We provide significantly improved fitting functions for the subhalo density profiles and structural parameters (V_{max} and r_{max}) that are unimpeded by numerical systematics and applicable to a wide range of parameter space. A model of the build-up and evolution of dark matter substructure: By combining our ESHDP model with a physically motivated prescription for the subhalo mass stripping rate, we introduce a state-of-the-art model of the mass evolution of individual subhaloes. This model has been calibrated to reproduce the mass trajectories of subhaloes in the DASH simulations. We incorporate this treatment of the subhalo internal structure and mass evolution into the recently released SatGen semi-analytical model. SatGen combines (i) analytical halo merger trees, (ii) a recipe for initial subhalo orbits at infall, (iii) an orbit integrator (which captures dynamical friction), and (iv) our DASH-calibrated tidal evolution model in order to ultimately capture the build-up and evolution of populations of dark matter substructure. We also develop a model of artificial disruption that reproduces the statistical properties of disruption in the Bolshoi simulation. Using the DASH-calibrated SatGen framework, we generate independent predictions for key quantities in small-scale cosmology, including the evolved subhalo mass function, subhalo radial abundance, and the substructure mass fraction and study how these quantities are impacted by artificial disruption and mass resolution limits. We find that artificial disruption affects these quantities at the 10--20% level, ameliorating previous concerns that it may suppress the SHMF by as much as a factor of two. We demonstrate that semi-analytical substructure modeling must include orbit integration in order to properly account for splashback haloes, which make up roughly half of the subhalo population. We show that the resolution limit of N-body simulations, rather than artificial disruption, is the primary cause of the radial bias in subhalo number density found in dark matter-only simulations. Hence, we conclude that the mass resolution remains the primary limitation of using such simulations to study subhaloes. The impact of a galactic disc on the subhalo population: Numerical simulations have shown that the formation of a central disc can drastically reduce the abundance of substructure compared to a dark matter-only simulation, which has been attributed to enhanced destruction of substructure due to disc shocking. We examine the impact of discs on substructure using SatGen. Using a sample of 10,000 merger trees of Milky-Way like haloes, we study the demographics of subhaloes that are evolved under a range of composite halo--disc potentials with unprecedented statistical power. We find that the overall subhalo abundance is relatively insensitive to properties of the disc aside from its total mass. For a disc that contains 5% of M_{vir}, the mean subhalo abundance within r_{vir} is suppressed by roughly less than 10% relative to the no-disc case, a difference that is dwarfed by halo-to-halo variance. For the same disc mass, the abundance of subhaloes within 50 kpc is reduced by ~30%. We argue that the disc mainly drives excess mass loss for subhaloes with small pericentric radii and that the impact of disc shocking is negligible. The three subhalo-focused studies described above constitute the primary thrust of this dissertation. However, the analytical Monte Carlo merger tree method, which is a key component of SatGen, has additional utility beyond the realm of subhalo studies. Indeed, an overarching theme of this program is that variation in assembly histories propagates to substantial halo-to-halo variance in many quantities of astrophysical and cosmological interest. We expand on this motif in the following two studies. The impact of assembly history variance on cluster scaling relations: X-ray and microwave cluster scaling relations are immensely valuable for cosmological analysis. However, their power is limited by astrophysical systematics that bias mass estimates and introduce additional scatter. Turbulence injected into the intracluster medium via mass assembly contributes substantially to cluster non-thermal pressure support, a significant source of such uncertainties. We use an analytical model to compute the assembly-driven non-thermal pressure profiles of haloes based on Monte Carlo-generated accretion histories (leveraging the same method that is used to generate merger trees in SatGen). We introduce a fitting function for the average non-thermal pressure fraction profile, which exhibits minimal dependence on redshift at fixed peak height. Using the model, we predict deviations from self-similarity and the intrinsic scatter in the Sunyaev--Zel'dovich effect observable-mass scaling relation (Y_{SZ}-M) due solely to inter-cluster variation in mass accretion histories. We study the dependence of Y_{SZ}-M on aperture radius, cosmology, redshift, and mass limit. The model predicts 5--9% scatter in Y_{SZ}-M at z=0, increasing as the aperture used to compute Y_{SZ} increases from R_{500c} to 5R_{500c}. The predicted scatter lies slightly below that of studies based on non-radiative hydro-simulations, illustrating that assembly history variance is likely responsible for a substantial fraction of scatter in Y_{SZ}-M. This should be regarded as a lower bound, which will likely increase with the use of an updated gas density model that incorporates a more realistic response to halo assembly. As redshift increases, Y_{SZ}-M deviates more from self-similarity and scatter increases. We show that the Y_{SZ}-M residuals correlate strongly with the recent halo mass accretion rate, potentially providing an opportunity to infer the latter. Estimating cluster masses via machine learning: We present a machine-learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2041 clusters from the Magneticum simulations. We train a random forest (RF) regressor, an ensemble learning method based on decision tree regression, to predict cluster masses using an input feature set. The feature set uses core-excised X-ray luminosity and a variety of morphological parameters, including surface brightness concentration, smoothness, asymmetry, power ratios, and ellipticity. The regressor is cross-validated and calibrated on a training sample of 1615 clusters (80% of sample), and then results are reported as applied to a test sample of 426 clusters (20% of sample). This procedure is performed for two different mock observation series in an effort to bracket the potential enhancement in mass predictions that can be made possible by including dynamical state information. The first series is computed from idealized Chandra-like mock cluster observations, with high spatial resolution, long exposure time (1 Ms), and the absence of background. The second series is computed from realistic-condition eROSITA mocks with lower spatial resolution, short exposures (2 ks), instrument effects, and background photons modeled. We report a 20% reduction in the mass estimation scatter when either series is used in our RF model compared to a standard regression model that only employs core-excised luminosity. The morphological parameters that hold the highest feature importance are smoothness, asymmetry, and surface brightness concentration. Hence these parameters, which encode the dynamical state of the cluster, can be used to make more accurate predictions of cluster masses in upcoming surveys, offering a crucial step forward for cosmological analyses.