Merger tree codes are routinely used to follow the growth and merger of dark matter haloes in simulations of cosmic structure formation. Whereas in Srisawat et. al. we compared the trees built using a wide variety of such codes, here we study the influence of the underlying halo catalogue upon the resulting trees. We observe that the specifics of halo finding itself greatly influences the constructed merger trees. We find that the choices made to define the halo mass are of prime importance. For instance, amongst many potential options different finders select self-bound objects or spherical regions of defined overdensity, decide whether or not to include substructures within the mass returned and vary in their initial particle selection. The impact of these decisions is seen in tree length (the period of time a particularly halo can be traced back through the simulation), branching ratio (essentially the merger rate of subhaloes) and mass evolution. We therefore conclude that the choice of the underlying halo finder is more relevant to the process of building merger trees than the tree builder itself. We also report on some built-in features of specific merger tree codes that (sometimes) help to improve the quality of the merger trees produced.
Merger trees are routinely used to follow the growth and merging history of dark matter haloes and subhaloes in simulations of cosmic structure formation. Srisawat et al. compared a wide range of merger-tree-building codes. Here we test the influence of output strategies and mass resolution on tree-building. We find that, somewhat surprisingly, building the tree from more snapshots does not generally produce more complete trees; instead, it tends to shorten them. Significant improvements are seen for patching schemes that attempt to bridge over occasional dropouts in the underlying halo catalogues or schemes that combine the halo-finding and tree-building steps seamlessly. The adopted output strategy does not affect the average number of branches (bushiness) of the resultant merger trees. However, mass resolution has an influence on both main branch length and the bushiness. As the resolution increases, a halo with the same mass can be traced back further in time and will encounter more small progenitors during its evolutionary history. Given these results, we recommend that, for simulations intended as precursors for galaxy formation models where of the order of 100 or more snapshots are analysed, the tree-building routine should be integrated with the halo finder, or at the very least be able to patch over multiple adjacent snapshots.
Several hundred million years after the Big Bang, the Epoch of Reionisation(EoR) started as the photons from the first objects ionised neutral baryons in the Universe. The observations such as the Gunn-Peterson troughs in quasar absorption spectra and the linear polarisation of the cosmic microwave background (CMB) impose strong constraints on reionisation models of the EoR. Recent data provide the rest-frame ultraviolet luminosity of galaxies up to redshift 10. However, the observation of star formations in low mass galaxies is still not practicable. Their star formations are expected to be suppressed by the increase of ionised baryons and greatly affect the reionisation models.
We develop a flexible pipeline which utilises the Munich Semi-Analytic Model of galaxy formation, L-Galaxies, and a semi-numerical modelling of cosmic reionisation. This combination allows us to create a self-consistent reionisation simulation in computational models of galaxy formation. We use this pipeline on a high resolution cosmological Nbody simulation to produce the redshift evolution of the star forming galaxies during the EoR. Comparisons of the properties of mock galaxies and the growth of ionised hydrogen bubbles suggest that the reionisation history heavily depends on the suppression models used in the modeling of dwarf galaxy formation. During this research, some numerical flaws of merger tree generation algorithms were identified. We investigated the origins of these problems and present suggestions for solving them.
When following the growth of structure in the Universe, we propose replacing merger trees with merger graphs, in which haloes can both merge and split into separate pieces. We show that this leads to smoother mass growth and eliminates catastrophic failures in which massive haloes have no progenitors or descendants. For those who prefer to stick with merger trees, we find that trees derived from our merger graphs have similar mass growth properties to previous methods, but again without catastrophic failures. For future galaxy formation modelling, two different density thresholds can be used to distinguish host haloes (extended galactic haloes, groups and clusters) from higher-density subhaloes: sites of galaxy formation.
Merging haloes with similar masses (i.e. major mergers) pose significant challenges for halo finders. We compare five halo-finding algorithms' (ahf, hbt, rockstar, subfind, and velociraptor) recovery of halo properties for both isolated and cosmological major mergers. We find that halo positions and velocities are often robust, but mass biases exist for every technique. The algorithms also show strong disagreement in the prevalence and duration of major mergers, especially at high redshifts (z > 1). This raises significant uncertainties for theoretical models that require major mergers for, e.g. galaxy morphology changes, size changes, or black hole growth, as well as for finding Bullet Cluster analogues. All finders not using temporal information also show host halo and subhalo relationship swaps over successive timesteps, requiring careful merger tree construction to avoid problematic mass accretion histories. We suggest that future algorithms should combine phase-space and temporal information to avoid the issues presented.
We propose a common terminology for use in describing both temporal merger trees and spatial structure trees for dark-matter halos. We specify a unified data format in HDF5 and provide example I/O routines in C, FORTRAN and PYTHON.
A halo merger tree forms the essential backbone of a semi-analytic model for galaxy formation and evolution. Recent studies have pointed out that extracting merger trees from numerical simulations of structure formation is non-trivial; different tree building algorithms can give differing merger histories. These differences should be carefully understood before merger trees are used as input for models of galaxy formation. We investigate the impact of different halo merger trees on a semi-analytic model. We find that the z=0 galaxy properties in our model show differences between trees when using a common parameter set. The star formation history of the Universe and the properties of satellite galaxies can show marked differences between trees with different construction methods. Independently calibrating the semi-analytic model for each tree can reduce the discrepancies between the z=0 global galaxy properties, at the cost of increasing the differences in the evolutionary histories of galaxies. Furthermore, the underlying physics implied can vary, resulting in key quantities such as the supernova feedback efficiency differing by factors of 2. Such a change alters the regimes where star formation is primarily suppressed by supernovae. Therefore, halo merger trees extracted from a common halo catalogue using different, but reliable, algorithms can result in a difference in the semi-analytic model. Given the uncertainties in galaxy formation physics, however, these differences may not necessarily be viewed as significant.
We present estimates of the nonlinear bias of cosmological halo formation, spanning a wide range in the halo mass from $\sim 10^{5} M_\odot$ to $\sim 10^{12} M_\odot$, based upon both a suite of high-resolution cosmological N-body simulations and theoretical predictions. The halo bias is expressed in terms of the mean bias and stochasticity as a function of local overdensity ($\delta$), under different filtering scales, which is realized as the density of individual cells in uniform grids. The sampled overdensities span a range wide enough to provide the fully nonlinear bias effect on the formation of haloes. A strong correlation between $\delta$ and halo population overdensity $\delta_h$ is found, along with sizable stochasticity. We find that the empirical mean halo bias matches, with good accuracy, the prediction by the peak-background split method based on the excursion set formalism, as long as the empirical, globally-averaged halo mass function is used. Consequently, this bias formalism is insensitive to uncertainties caused by varying halo identification schemes, and can be applied generically. We also find that the probability distribution function of biased halo numbers has wider distribution than the pure Poisson shot noise, which is attributed to the sub-cell scale halo correlation. We explicitly calculate this correlation function and show that both overdense and underdense regions have positive correlation, leading to stochasticity larger than the Poisson shot noise in the range of haloes and halo-collapse epochs we study.
We investigate the formation, growth, merger history, movement, and destruction of cosmic voids detected via the watershed transform code VIDE in a cosmological N-body dark matter {\Lambda}CDM simulation. By adapting a method used to construct halo merger trees, we are able to trace individual voids back to their initial appearance and record the merging and evolution of their progenitors at high redshift. For the scales of void sizes captured in our simulation, we find that the void formation rate peaks at scale factor 0.3, which coincides with a growth in the void hierarchy and the emergence of dark energy. Voids of all sizes appear at all scale factors, though the median initial void size decreases with time. When voids become detectable they have nearly their present-day volumes. Almost all voids have relatively stable growth rates and suffer only infrequent minor mergers. Dissolution of a void via merging is very rare. Instead, most voids maintain their distinct identity as annexed subvoids of a larger parent. The smallest voids are collapsing at the present epoch, but void destruction ceases after scale factor 0.3. In addition, voids centers tend to move very little, less than 0.01 of their effective radii per ln a, over their lifetimes. Overall, most voids exhibit little radical dynamical evolution; their quiet lives make them pristine probes of cosmological initial conditions and the imprint of dark energy.
We present a clustering comparison of 12 galaxy formation models [including semi-analytic models (SAMs) and halo occupation distribution (HOD) models] all run on halo catalogues and merger trees extracted from a single Λ cold dark matter N-body simulation. We compare the results of the measurements of the mean halo occupation numbers, the radial distribution of galaxies in haloes and the two-point correlation functions (2PCF). We also study the implications of the different treatments of orphan (galaxies not assigned to any dark matter subhalo) and non-orphan galaxies in these measurements. Our main result is that the galaxy formation models generally agree in their clustering predictions but they disagree significantly between HOD and SAMs for the orphan satellites. Although there is a very good agreement between the models on the 2PCF of central galaxies, the scatter between the models when orphan satellites are included can be larger than a factor of 2 for scales smaller than 1 h−1 Mpc. We also show that galaxy formation models that do not include orphan satellite galaxies have a significantly lower 2PCF on small scales, consistent with previous studies. Finally, we show that the 2PCF of orphan satellites is remarkably different between SAMs and HOD models. Orphan satellites in SAMs present a higher clustering than in HOD models because they tend to occupy more massive haloes. We conclude that orphan satellites have an important role on galaxy clustering and they are the main cause of the differences in the clustering between HOD models and SAMs.
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