Orientation of satellites galaxies: massive hosts versus the cosmic web 

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Small-scale physical recipes for realistic galactic dynamics.

Once the dynamics is computed for the gas and the particles, simulating a physical universe still requires to compute the non-linear physics that govern small scales of the Universe. Since our resolution is limited to 1 kpc, many of these processes actually occur on typical scales smaller than the smallest cell in the simulation. They are consecutively labeled as « sub-grid processes », hence only modeled through their effective impact on cell scales. Let us review all such processes implemented in Horizon-AGN .

Gas cooling and heating

Photons interact with electrons -either bound in an atom or free- in many ways that can impact the overall energy of the system. Specifically, photons can excite bound electrons to either higher energy bound states which will soon after decay radiating away the excess energy, or to unbound states which may lead to the subsequent recombination of the electron with another photon (ionization/ recombination). Free electrons can also transfer their kinetic energy to background photons through two channels: bremsstrahlung (fly-by braking) or inverse Compton scattering (head-on collision). These processes, each of which dominates in a specific temperature range, therefore reduce the internal energy of the gas. This loss of energy e˙ therefore depends on the number density of protons np and electrons ne: e˙ ∝ nenp .
In the temperature range 104 − −105 K, gas is at ionization equilibrium, leading to a plasma where the number density of electrons and the proton number density are related through specific coefficients that account for the rates of spontaneous emission, absorption and stimulated emission respectively. The loss of energy writes e˙ = fcool(T )npne where fcool(T ) is a cooling rate that encapsulates the efficiency of each process at a given temperature T.

Star formation and stellar feedback

Stars form from the collapse of giant molecular clouds or ultra-dense infrared dark clouds (under Jeans instability) emerging from the cooling of high-density gas. This suggests that the star formation rate must be a function of the local gas density, a relationship that reveals surprisingly tight in observations (Kennicutt (1998)) which found it to be close to ρst˙ar ∝ ρ3/2 gas. This behavior can be understood as the result of the star formation rate following a Schmidt law: ρ˙star = ǫ∗ρ/tff , (1.14).
where ρ˙star is the star formation rate density, ǫ∗ = 0.02 (Kennicutt, 1998; Krumholz & Tan, 2007) the constant star formation efficiency, and tff the local free-fall time of the gas: tff = 3π 32Gρ . (1.15)
This is how star formation is modeled in Horizon-AGN .
However, observations also reveal that stars form only in regions where the gas density exceeds a given threshold that corresponds to the transition from atomic hydrogen to molecular hydrogen( Kennicutt, 1998; Wong & Blitz, 2002). Following on this behavior, although with some corrections to overcome the limited resolution of the simulation, star formation in Horizon-AGN is allowed in regions which exceed a gas Hydrogen number density threshold of n0 = 0.1Hcm−3.
In such regions, at each time step, a small fraction of gas is converted into star particles the density of which is given by the Schmidt law, and whose individual masses are multiple of the minimum mass M∗ = ρ0x3 ≃ 2 × 106M⊙. The multiple is drawn from a Poissonian random process (Rasera & Teyssier, 2006; Dubois & Teyssier, 2008).

Haloes and galaxies: Structure identification and merging

Identification Haloes and galaxies are identified from DMparticles and star particles respectively using HaloMaker (Tweed et al. , 2009, based on) with the AdaptaHOP algorithm (Aubert et al. , 2004). This subsection only summarizes the main features of the algorithm.
This method identifies structures from the particle positions only, no further correction is performed based on the velocities. Its great advantage is however its ability to detect sub-structures. It first computes the density at each particle position by finding its N nearest neighbors and integrating their contribution to the local density using the standard SPH (smoothed particle hydrodynamics) spline kernel (Monaghan (1992). A total of 20 neighbours were used to compute the local density of each particle in our post-processing of Horizon-AGN .
Then the algorithm hops from one particle to its highest density neighbor until it reaches a local maximum. Once all the local maxima of the field are found, a peak patch around each maximum is defined as the set of particles above a well-suited density threshold (ρ/¯ρ > ρth where ¯ρ is the average of the total matter density) that share this local maximum. In the following work, I chose ρth = 178 to identify clear collapsed, virialised structures: haloes and galaxies. At this point, detected overdensities still need to be discriminated into main structures and sub-structures of various levels.

Synthetic galaxies in Horizon-AGN

Applying this identification process in Horizon-AGN and selecting only galactic structures identified with more than 50 particles, I produce catalogues of around ∼ 150 000 galaxies and ∼ 300 000 haloes at each snapshot of the simulation for redshifts 0 < z < 5. For each galaxy or halo, HaloMaker produces a list of all the particles (star or DM) in the structure with their position, age, mass and velocity, along with some global properties such as the total mass of the structure or its virial radius.
These data allow to compute numerous more elaborate properties such shape parameters (inertia tensor, bulge-to-disc ratios, triaxiality) or rest-frame colors from AB magnitudes. The computation of such specific properties will be detailed in each chapter when relevant to the study. However, a few general comments can be made prior to a more detailed analysis:
• Although the limited resolution used for the hydrodynamics and intense AGN feedback prevent the formation of very thin disks with well defined spiral patterns, Horizon-AGN recovers a wide morphological diversity for galaxies at all redshifts with disk, ellipticals and spheroids, covering a wide range of masses and colors. A few examples are presented on Fig. 1.1 .
• The mass function in Horizon-AGN is compatible with observations down to lowest redshifts although it tends to overestimate the low-mass range by a factor ≃ 3 as can bee seen on Fig.. 1.2. It should be noted however that the rate of AGN feedback and supernovae feedback are tuned so as to bend the mass function in the high-mass and low-mass range respectively and obtain this compatible mass function.
• Horizon-AGN features the large-scale pattern of the cosmic web, with filaments and walls surrounding voids and connecting halos, the gas following very closely the distribution of the underlying dark matter on largest scales. A projected map of half the simulation volume and a smaller sub-region are shown in Fig. 1.3.

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The spin of dark haloes: a mass segregated distribution

The consensus that has emerged from the aforementioned studies is that the orientation of the spin of the dark haloes is imprinted by the geometry of the surrounding large scale structures, more specifically the nearby cosmic filaments, following two distinct mass dependent trends:
• low-mass haloes tend to display a spin parallel to the nearest large-scale filament.
• more massive haloes are more likely to have a spin orthogonal to the nearest filament.
This is not an absolute trend but a mild -though compelling- statistical effect, therefore better described by the evolution of the excess probability ξ of given deviations angles. While previous works had pointed out strong hints of such a mass-segregation, Codis et al. (2012) made the first robust quantitative estimation of such an angular distribution and confirmed with high relevancy the existence of a smooth transition from alignment to perpendicularity as the halo mass increases. Studying the orientation of 40 millions dark haloes in the cosmological N-body simulation 4π and making use of the same state-of-the-art filament detection methods presented in Chapter 1(Sousbie et al. (2009)), they constrained the estimation of the halo transition mass around 5 1012 M⊙ and the highest alignment excess probability for the cosine of the angle between the halo spin and the direction of its filament around ξ = 20%.
They suggest a scenario involving the winding around of cosmic flows conjoint to the filamentary collapse to justify the spinning of small haloes parallel to their filament, and relying on Tidal Torque Theory (Hoyle, 1949; Peebles, 1969; Doroshkevich, 1970; White, 1984; Porciani et al. , 2002a,b) (possibly relayed through mergers) to flip more massive haloes perpendicular to it. In the next sections, the predictions of Tidal Torque Theory are presented with its subsequent improvements and develop the most up-to-date version of this scenario

Table of contents :

0.1 Structure formation in the early universe: linear perturbation theory
0.2 Spherical collapse and Press-Schechter theory
0.3 The cosmic web
0.4 Galactic morphologies
0.5 Structure of this Thesis
1 Numerical Methods 
1.1 Simulating the universe on cosmological scales
1.1.1 RAMSES: basic features
1.1.2 Small-scale physical recipes for realistic galactic dynamics.
1.2 Structure detection and identification in Horizon-AGN
1.2.1 Haloes and galaxies: Structure identification and merging
1.2.2 Synthetic galaxies in Horizon-AGN
1.2.3 The numerical cosmic web
2 Galactic spin alignments induced by the cosmic web 
2.1 Orientation of dark haloes in the cosmic web
2.1.1 The spin of dark haloes: a mass segregated distribution
2.1.2 Tidal Torque Theory
2.1.3 A dynamical scenario
2.1.4 Mergers versus smooth accretion
2.1.5 From haloes to galaxies
2.2 Tracing galactic spin swings in the cosmic web
2.2.1 Numerical Methods
2.2.2 Evolution tracers
2.2.3 Alignments in Horizon-AGN
2.2.4 Comparison to observations
2.3 How mergers drive spin swings in the cosmic web
2.3.1 Tracking mergers in Horizon-AGN
2.3.2 Mergers, stellar mass and spin in Horizon-AGN: close-up case studies
2.3.3 Mergers and smooth accretion on spin orientation
2.3.4 Mergers and smooth accretion on acquisition of spin.
2.4 Conclusion
3 Orientation of satellites galaxies: massive hosts versus the cosmic web 
3.1 An overview of satellite galaxies
3.1.1 The formation of satellite galaxies in CDM cosmology
3.1.2 The distribution of satellite galaxies
3.2 Satellites in Horizon-AGN
3.2.1 Identifying central galaxies and satellites
3.2.2 Tracing the evolution of satellites in the halo: synthetic colors.
3.3 Statistical properties of the orientation of satellites
3.3.1 Methods and variables
3.3.2 Results
3.4 A dynamical scenario : satellites migrating into the halo.
3.4.1 Scenario
3.4.2 Corotation of satellites
3.4.3 Evolution of satellites within the halo
3.5 Implications for observations.
3.5.1 Color selection
3.5.2 Signal on smaller scales
3.5.3 Effects of the shape of the central host and high-z alignments.
3.6 Conclusion.
4 The rise and fall of stellar disks at z > 1 
4.1 Inflows and galaxy encounters: an overview
4.1.1 Disc galaxies: evolution of the Hubble sequence with redshift
4.1.2 Violent Disc Instability: the path to compact spheroids
4.1.3 Mergers
4.1.4 Dry or wet mergers? Extended spheroids and massive disks.
4.2 Characterizing different types of mergers in Horizon-AGN
4.2.1 Characterizing the morphology of galaxies
4.2.2 Gas content of high-z galaxies
4.2.3 Merger rates: from observations to simulation
4.3 Size growth of galaxies
4.3.1 Galactic stellar density
4.3.2 Galactic half-mass radius
4.3.3 Impact of initial gas fraction and morphology
4.4 Impact on morphologies
4.4.1 Smooth accretion
4.4.2 Mergers
4.5 Conclusion
4.6 Main results
4.7 Prospects
4.7.1 Sorting out the merger zoo
4.7.2 Gas inflows: feeding galaxies into diverse morphologies ?
4.7.3 Distribution of satellites


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