Related papers: Machine Learning and Cosmological Simulations II: …
We present a dataset built for machine learning applications consisting of galaxy photometry, images, spectroscopic redshifts, and structural properties. This dataset comprises 286,401 galaxy images and photometry from the Hyper-Suprime-Cam…
We present results of large N-body-hydrodynamic simulations of galaxy formation. Our simulations follow the formation of galaxies in cubic volumes of side 100Mpc, in two versions of the cold dark matter (CDM) cosmogony: the standard,…
Cosmological inference with large galaxy surveys requires theoretical models that combine precise predictions for large-scale structure with robust and flexible galaxy formation modelling throughout a sufficiently large cosmic volume. Here,…
Galaxy assembly bias, the correlation between galaxy properties and halo properties at fixed halo mass, could be an important ingredient in halo-based modelling of galaxy clustering. We investigate the central galaxy assembly bias by…
We study the effects of applying observational techniques to derive the properties of simulated galaxies, with the aim of making an unbiased comparison between observations and simulations. For our study, we used fifteen galaxies simulated…
Hydrodynamic simulations have become irreplaceable in modern cosmology for exploring complex systems and making predictions to steer future observations. In Chapter 1, we begin with a philosophical discussion on the role of simulations in…
Hydrodynamic simulations are powerful tools for studying galaxy formation. However, it is crucial to test and improve the sub-grid physics underlying these simulations by comparing their predictions with observations. To this aim,…
Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They have also proven to be an efficient alternative for generating…
The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now…
The interpretive power of the newest generation of large-volume hydrodynamical simulations of galaxy formation rests upon their ability to reproduce the observed properties of galaxies. In this second paper in a series, we employ bulge+disc…
Modern hydrodynamical simulations offer nowadays a powerful means to trace the evolution of the X-ray properties of the intra-cluster medium (ICM) during the cosmological history of the hierarchical build up of galaxy clusters. In this…
This is the second paper in a series presenting the results from a 500 $h^{-1}$Mpc large constrained hydro-dynamical simulation of the local Universe (SLOW). The initial conditions are based on peculiar velocities derived from the…
In this thesis, we aim to further elucidate the phenomenon of galaxy evolution in the environment of galaxy clusters using the methodology of numerical simulations. For that, we have developed hydrodynamic models in which idealized gas-rich…
The balance of evidence indicates that individual galaxies and groups or clusters of galaxies are embedded in enormous distributions of cold, weakly interacting dark matter. These dark matter 'halos' provide the scaffolding for all luminous…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
We study the Intra-Halo Stellar Component (IHSC) of Milky Way-mass systems up to galaxy clusters in the Horizon-AGN cosmological hydrodynamical simulation. We identify the IHSC using an improved phase-space galaxy finder algorithm which…
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…
We describe a phenomenological model for molecular hydrogen formation suited for applications in galaxy formation simulations, which includes on-equilibrium formation of molecular hydrogen on dust and approximate treatment of both its…
Cosmologists at the Institute of Computational Cosmology, Durham University, have developed a state of the art model of galaxy formation known as Galform, intended to contribute to our understanding of the formation, growth and subsequent…
The problem of galaxy formation and its dependence on thermodynamic properties is addressed by using Eulerian hydrodynamic numerical simulations of large scale structure formation. Global galaxy properties are explored in simulations…