The formation and evolution of galaxies is an interesting subject to study because it incorporates astrophysics from all scales, from the initial perturbations in the early universe creating the large scale structures that produce galaxies, right down to the evolution of stellar populations and their manipulation of the host galaxy. Simulations of galaxy formation allow us to test the various physical recipes against that which is observed in order to build a true and proper picture of what is happening in the real universe. L-Galaxies is a semi-analytic model of galaxy formation built on top of the merger trees from the Millennium dark matter simulation, and is constrained to match certain key observations at low redshift by applying a Monte Carlo Markov Chain (MCMC) method to constrain the free parameters. In using the model to make high redshift predictions of the stellar mass function, UV luminosity function and star formation rate distribution function we found that the model starts to deviate from observational constraints at the highest redshifts, particularly in high mass galaxies. In the case of the UV luminosity function, this is because the current dust model is calibrated at low redshift and lacks sophistication in that it only depends on the cold gas mass and the density of metals. To improve on this we implement a physically motivated dust model that traces the formation of dust from stellar sources, such as in the stellar winds of AGB stars and in the supernovae remnants of massive stars, the growth of dust inside molecular clouds, and the destruction of dust due to supernovae explosions. The model is fully integrated into L-Galaxies such that the evolution of dust is included in all the recipes relevant to the formation and evolution of galaxies, including: star formation; radiative feedback; cooling and reheating; and both major and minor mergers. Our results show a good fit to observations of the dust mass in galaxies both in the local universe and out to high redshift and we note a similar conclusion as in the literature that dust growth inside molecular clouds is not only necessary but the dominant source of the dust mass in these galaxies. However, stellar sources of dust can not be neglected as molecular clouds must first be seeded by dust grains in order for accretion to occur. This could be important in the very early universe, perhaps for the first galaxies that will hopefully be observed by JWST in the future, because these galaxies may not have had sufficient time to seed their molecular clouds and as such the dust produced by these stellar sources would be important for calculating the galaxies true observed luminosity. We finish by discussing the limitations of the model and discuss areas for possible improvement as well as the next steps in using this to better predict the luminosity of galaxies in future models.

In this thesis we use both observations and modelling to explore the gas content of galaxies. We use the L-Galaxies semi-analytic model to simultaneously match the Hɪ and stellar mass properties of model galaxies to observations using Markov Chain Monte Carlo methods. We add the observed Hɪ mass function as an extra model constraint and successfully match the Hɪ and stellar mass functions. However, the fit to the star formation properties has been weakened compared to without the Hɪ constraint. We suggest that this problem may be partially resolved by forming stars out of only H2 gas instead of the total cold gas. The environment in which a galaxy resides can affect its evolution. We use the counts in a fixed size cylinder method to estimate 3 environment measures for the GAMA survey. We use density and edge corrections to allow us to calculate estimates for every galaxy out to z = 0.4 in our flux limited sample. We then use these estimates to examine the effect of environment on the luminosity and stellar mass functions. Using Hɪ observations of the groups and galaxies in the ALFALFA and GAMA surveys we calculate Hɪ masses using the stacking technique. The use of the stacking technique has allowed us to exploit survey data that would not otherwise be possible. We stack galaxies in halo mass bins and calculate the Hɪ to halo mass fraction as a function of halo mass. We see a steady decline in the Hɪ fraction as we move to higher mass halos. These are the highest density environments where there is less cold gas. Combining this fraction with the halo mass function we are able to calculate a lower limit value for ΩHɪ of 1.8 ± 0.39 x 10-4h-¹.

Evolution of galaxies is one of the most important topics in astronomy to understand how the universe has been evolving. In particular, galaxy groups are important because they are the observable equivalent of dark matter (DM) haloes, and thus offer a direct insight into the physics that has occurred in the DM haloes in the Universe up to the present day. Isolated galaxies are crucial for studying intrinsic and secular processes able to affect the structure, morphology, and dynamics of galaxies for obtaining clear relationships and correlations to be confronted with the model predictions. The main goal of this work is to characterize the GAMA G3Cv1 galaxy groups catalogue and the UNAM-KIAS catalogue of isolated galaxies by one of the most important statistical studies, the galaxy Luminosity Function (LF), that helps to constrain the models of formation and evolution of galaxies. LFs have been estimated for galaxies in groups and isolated galaxies. The LF for groups has been characterized by the physical properties of the groups (mass and velocity dispersion), the photometry (colour), the morphological type and eleven wavelengths from the far infra-red to the ultra violet. The LF estimated for the isolated galaxies is characterized by morphology and the colour in the five SDSS bands. The results obtained constrain more effectively the formation and evolution models of the universe than previous samples. The differences between both catalogues are presented in the conclusions. Additionally, the galaxy morphology is one of the no well understood problems in the galaxy evolution process to support the hierarchical model of formation of large objects. In this work, a classification based on the colour and concentration of light was considered. However, due to the low resolution of the images, the confidence of this classification was only ~60%.

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.

During the first billion years after the big bang, the large-scale cosmic web of structures we see today began to form. This was followed by the first stars and galaxies, which brought an end to the Dark Ages. These first luminous sources are thought to be the prime candidates which fuelled cosmic reionization, the last major phase transition of the Universe, from a neutral inter-galactic medium following recombination to the ionized state it remains in today. The physical processes which drive reionization encapsulate several areas of research, from cosmology and galaxy formation to radiative transfer and atomic physics. Even with the wealth of present-day observational information at our disposal, the processes are still not fully understood. Therefore we cannot model reionization analytically, instead turning to numerical simulations using observations to constrain our models. We perform a suite of fully-coupled radiation-hydrodynamical simulations of galaxy formation in cosmological volumes to probe the self-feedback of galaxies during the Epoch of Reionization. This research focuses on the transport of gas from the intergalactic medium onto dark matter halos, and consequences for semi-analytical models of galaxy formation. To improve on existing methods, which constrain the halo baryon fraction during reionization, we develop and train an artificial neural network to predict this quantity based on the physical properties of haloes. We demonstrate that this model is independent of redshift and reionization history, and can be trivially incorporated into semi-analytical models of galaxy formation. We further probe the physical processes which allow ionizing photons to escape from galaxies to reionize the Universe, specifically how stellar evolution uncertainties such as binary populations influence this process. Finally, we investigate to what extent a relative supersonic drift velocity between baryons and dark matter, present at recombination, may suppress the formation of the first objects and fundamentally alter their evolution. To do this, we develop a new method based on cosmological zoom simulations to include this effect in boxes much larger than the coherence length of the relative velocity for the first time.