We use cosmological simulations to study a characteristic evolution pattern of high redshift galaxies. Early, stream-fed, highly perturbed, gas-rich discs undergo phases of dissipative contraction into compact, star-forming systems ("blue" nuggets) at z ∼ 4 − 2. The peak of gas compaction marks the onset of central gas depletion and inside-out quenching into compact ellipticals (red nuggets) by z ∼ 2. These are sometimes surrounded by gas rings or grow extended dry stellar envelopes. The compaction occurs at a roughly constant specific starformation rate (SFR), and the quenching occurs at a constant stellar surface density within the inner kpc (Σ 1 ). Massive galaxies quench earlier, faster, and at a higher Σ 1 than lower-mass galaxies, which compactify and attempt to quench more than once. This evolution pattern is consistent with the way galaxies populate the SFR-size-mass space, and with gradients and scatter across the main sequence. The compaction is triggered by an intense inflow episode, involving (mostly minor) mergers, counter-rotating streams or recycled gas, and is commonly associated with violent disc instability. The contraction is dissipative, with the inflow rate >SFR, and the maximum Σ 1 anti-correlated with the initial spin parameter . The central quenching is triggered by the high SFR and stellar/supernova feedback (maybe also AGN feedback) due to the high central gas density, while the central inflow weakens as the disc vanishes. Suppression of fresh gas supply by a hot halo allows the longterm maintenance of quenching once above a threshold halo mass, inducing the quenching downsizing.

We present a detailed comparison of fundamental dark matter halo properties retrieved by a substantial number of different halo finders. These codes span a wide range of techniques including friends‐of‐friends, spherical‐overdensity and phase‐space‐based algorithms. We further introduce a robust (and publicly available) suite of test scenarios that allow halo finder developers to compare the performance of their codes against those presented here. This set includes mock haloes containing various levels and distributions of substructure at a range of resolutions as well as a cosmological simulation of the large‐scale structure of the universe. All the halo‐finding codes tested could successfully recover the spatial location of our mock haloes. They further returned lists of particles (potentially) belonging to the object that led to coinciding values for the maximum of the circular velocity profile and the radius where it is reached. All the finders based in configuration space struggled to recover substructure that was located close to the centre of the host halo, and the radial dependence of the mass recovered varies from finder to finder. Those finders based in phase space could resolve central substructure although they found difficulties in accurately recovering its properties. Through a resolution study we found that most of the finders could not reliably recover substructure containing fewer than 30–40 particles. However, also here the phase‐space finders excelled by resolving substructure down to 10–20 particles. By comparing the halo finders using a high‐resolution cosmological volume, we found that they agree remarkably well on fundamental properties of astrophysical significance (e.g. mass, position, velocity and peak of the rotation curve). We further suggest to utilize the peak of the rotation curve, vmax, as a proxy for mass, given the arbitrariness in defining a proper halo edge.

We describe simple useful toy models for key processes of galaxy formation in its most active phase, at z > 1, and test the approximate expressions against the typical behaviour in a suite of high-resolution hydro-cosmological simulations of massive galaxies at z = 4 − 1. We address in particular the evolution of (a) the total mass inflow rate from the cosmic web into galactic haloes based on the EPS approximation, (b) the penetration of baryonic streams into the inner galaxy, (c) the disc size, (d) the implied steady-state gas content and star-formation rate (SFR) in the galaxy subject to mass conservation and a universal star-formation law, (e) the inflow rate within the disc to a central bulge and black hole as derived using energy conservation and self-regulated Q ∼ 1 violent disc instability (VDI), and (f) the implied steady state in the disc and bulge. The toy models provide useful approximations for the behaviour of the simulated galaxies. We find that (a) the inflow rate is proportional to mass and to (1 + z) 5/2 , (b) the penetration to the inner halo is ∼ 50% at z = 4 − 2, (c) the disc radius is ∼ 5% of the virial radius, (d) the galaxies reach a steady state with the SFR following the accretion rate into the galaxy, (e) there is an intense gas inflow through the disc, comparable to the SFR, following the predictions of VDI, and (f) the galaxies approach a steady state with the bulge mass comparable to the disc mass, where the draining of gas by SFR, outflows and disc inflows is replenished by fresh accretion. Given the agreement with simulations, these toy models are useful for understanding the complex phenomena in simple terms and for back-of-the-envelope predictions.

We study the properties of giant clumps and their radial gradients in high-z disc galaxies using AMR cosmological simulations. Our sample consists of 770 snapshots in the redshift range z = 4 − 1 from 29 galaxies that at z = 2 span the stellar mass range (0.2 − 3) × 10 11 M ⊙ . Extended gas discs exist in 83% of the snapshots. Clumps are identified by gas density in 3D and their stellar and dark matter components are considered thereafter. While most of the overdensities are diffuse and elongated, 91% of their mass and 83% of their star-fromation rate (SFR) are in compact round clumps. Nearly all galaxies have a central, massive bulge clump, while 70% of the discs show off-center clumps, 3-4 per galaxy. The fraction of clumpy discs peaks at intermediate disc masses. Clumps are divided based on dark-matter content into in-situ and ex-situ, originating from violent disc instability (VDI) and minor mergers respectively. 60% of the discs are in a VDI phase showing off-center in-situ clumps, which contribute 1-7% of the disc mass and 5-45% of its SFR. The in-situ clumps constitute 75% of the off-center clumps in terms of number and SFR but only half the mass, each clump containing on average 1% of the disc mass and 6% of its SFR. They have young stellar ages, 100−400 Myr, and high specific SFR (sSFR), 1−10 Gyr −1 . They exhibit gradients resulting from inward clump migration, where the inner clumps are somewhat more massive and older, with lower gas fraction and sSFR and higher metallicity. Similar observed gradients indicate that clumps survive outflows. The ex-situ clumps have stellar ages 0.5 − 3 Gyr and sSFR ∼ 0.1 − 2 Gyr −1 , and they exhibit weaker gradients. Massive clumps of old stars at large radii are likely ex-situ mergers, though half of them share the disc rotation.

Context. In the past decade or so, using numerical N-body simulations to describe the gravitational clustering of dark matter (DM) in an expanding universe has become the tool of choice for tackling the issue of hierarchical galaxy formation. As mass resolution increases with the power of supercomputers, one is able to grasp finer and finer details of this process, resolving more and more of the inner structure of collapsed objects. This begs one to revisit time and again the post-processing tools with which one transforms particles into "invisible" dark matter haloes and from thereon into luminous galaxies. Aims. Although a fair amount of work has been devoted to growing Monte-Carlo merger trees that resemble those built from an N-body simulation, comparatively little effort has been invested in quantifying the caveats one necessarily encounters when one extracts trees directly from such a simulation. To somewhat revert the tide, this paper seeks to provide its reader with a comprehensive study of the problems one faces when following this route. Methods. The first step in building merger histories of dark matter haloes and their subhaloes is to identify these structures in each of the time outputs (snapshots) produced by the simulation. Even though we discuss a particular implementation of such an algorithm (called AdaptaHOP) in this paper, we believe that our results do not depend on the exact details of the implementation but instead extend to most if not all (sub)structure finders. To illustrate this point in the appendix we compare AdaptaHOP's results to the standard friend-of-friend (FOF) algorithm, widely utilised in the astrophysical community. We then highlight different ways of building merger histories from AdaptaHOP haloes and subhaloes, contrasting their various advantages and drawbacks. Results. We find that the best approach to (sub)halo merging histories is through an analysis that goes back and forth between identification and tree building rather than one that conducts a straightforward sequential treatment of these two steps. This is rooted in the complexity of the merging trees that have to depict an inherently dynamical process from the partial temporal information contained in the collection of instantaneous snapshots available from the N-body simulation. However, we also propose a simpler sequential "Most massive Substructure Method" (MSM) whose trees approximate those obtained via the more complicated non sequential method.

We present a detailed comparison of the substructure properties of a single Milky Way sized dark matter halo from the Aquarius suite at five different resolutions, as identified by a variety of different (sub)halo finders for simulations of cosmic structure formation. These finders span a wide range of techniques and methodologies to extract and quantify substructures within a larger non‐homogeneous background density (e.g. a host halo). This includes real‐space‐, phase‐space‐, velocity‐space‐ and time‐space‐based finders, as well as finders employing a Voronoi tessellation, Friends‐of‐Friends techniques or refined meshes as the starting point for locating substructure. A common post‐processing pipeline was used to uniformly analyse the particle lists provided by each finder. We extract quantitative and comparable measures for the subhaloes, primarily focusing on mass and the peak of the rotation curve for this particular study. We find that all of the finders agree extremely well in the presence and location of substructure and even for properties relating to the inner part of the subhalo (e.g. the maximum value of the rotation curve). For properties that rely on particles near the outer edge of the subhalo the agreement is at around the 20 per cent level. We find that the basic properties (mass and maximum circular velocity) of a subhalo can be reliably recovered if the subhalo contains more than 100 particles although its presence can be reliably inferred for a lower particle number limit of 20. We finally note that the logarithmic slope of the subhalo cumulative number count is remarkably consistent and <1 for all the finders that reached high resolution. If correct, this would indicate that the larger and more massive, respectively, substructures are the most dynamically interesting and that higher levels of the (sub)subhalo hierarchy become progressively less important.

The ever increasing size and complexity of data coming from simulations of cosmic structure formation demands equally sophisticated tools for their analysis. During the past decade, the art of object finding in these simulations has hence developed into an important discipline itself. A multitude of codes based upon a huge variety of methods and techniques have been spawned yet the question remained as to whether or not they will provide the same (physical) information about the structures of interest. Here we summarize and extent previous work of the "halo finder comparison project": we investigate in detail the (possible) origin of any deviations across finders. To this extent we decipher and discuss differences in halo finding methods, clearly separating them from the disparity in definitions of halo properties. We observe that different codes not only find different numbers of objects leading to a scatter of up to 20 per cent in the halo mass and V max function, but also that the particulars of those objects that are identified by all finders differ. The strength of the variation, however, depends on the property studied, e.g. the scatter in position, bulk velocity, mass, and the peak value of the rotation curve is practically below a few per cent, whereas derived quantities such as spin and shape show larger deviations. Our study indicates that the prime contribution to differences in halo properties across codes stems from the distinct particle collection methods and -to a minor extent -the particular aspects of how the procedure for removing unbound particles is implemented. We close with a discussion of the relevance and implications of the scatter across different codes for other fields such as semi-analytical galaxy formation models, gravitational lensing, and observables in general.

Merger trees follow the growth and merger of dark-matter haloes over cosmic history. As well as giving important insights into the growth of cosmic structure in their own right, they provide an essential backbone to semi-analytic models of galaxy formation. This paper is the first in a series to arise from the SUSSING MERGER TREES Workshop in which ten different tree-building algorithms were applied to the same set of halo catalogues and their results compared. Although many of these codes were similar in nature, all algorithms produced distinct results. Our main conclusions are that a useful merger-tree code should possess the following features: (i) the use of particle IDs to match haloes between snapshots; (ii) the ability to skip at least one, and preferably more, snapshots in order to recover subhaloes that are temporarily lost during merging; (iii) the ability to cope with (and ideally smooth out) large, temporary flucuations in halo mass. Finally, to enable different groups to communicate effectively, we defined a common terminology that we used when discussing merger trees and we encourage others to adopt the same language. We also specified a minimal output format to record the results.