Related papers: A graph-based Neural Network surrogate model for a…
Efficiently mapping baryonic properties onto dark matter is a major challenge in astrophysics. Although semi-analytic models (SAMs) and hydrodynamical simulations have made impressive advances in reproducing galaxy observables across…
Merger trees track the hierarchical assembly of dark matter halos across cosmic time and serve as essential inputs for semi-analytic models of galaxy formation. However, conventional methods for constructing merger trees rely on ad-hoc…
After more than five years of development, we present a new version of Dark Sage, a semi-analytic model (SAM) of galaxy formation that breaks the mould for models of its kind. Included among the major changes is an overhauled treatment of…
We investigate a series of galaxy properties computed using the merger trees and environmental histories from dark matter only cosmological simulations, using a semi-recurrent neural network producing self-consistent predictions of galaxy…
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…
A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of…
We present a new exploratory framework to model galaxy formation and evolution in a hierarchical universe by using machine learning (ML). Our motivations are two-fold: (1) presenting a new, promising technique to study galaxy formation, and…
This paper demonstrates that the stellar masses of galaxies in the Galaxy and Mass Assembly (GAMA) survey, originally derived via stellar population synthesis modelling, can be accurately predicted using only their absolute magnitudes and…
Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses ($\rm{M}_{\rm{halo}}$) must be inferred indirectly. We present a graph neural network (GNN) model for predicting $\rm{M}_{\rm{halo}}$…
The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a…
Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree'' structure. Previous work has shown these merger trees are…
We present a new cosmological galaxy formation model, $\nu^2$GC, as an updated version of our previous model $\nu$GC. We adopt the so-called "semi-analytic" approach, in which the formation history of dark matter halos is computed by ${\it…
We present a new Monte-Carlo algorithm to generate merger trees describing the formation history of dark matter halos. The algorithm is a modification of the algorithm of Cole et al (2000) used in the GALFORM semi-analytic galaxy formation…
Semi-analytic models (SAMs) are a promising means of tracking the physical processes associated with galaxy formation, but many of their approximations have not been rigorously tested. As part of the SMAUG (Simulating Multiscale…
Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings…
Modelling the growth histories of specific galaxies often involves generating the entire population of objects that arise in a given cosmology and selecting systems with appropriate properties. This approach is highly inefficient when…
Semi-analytical models (SAMs) are currently the best way to understand the formation of galaxies within the cosmic dark-matter structures. While they fairly well reproduce the local stellar mass functions, correlation functions and…
Semi-analytic models are best suited to compare galaxy formation and evolution theories with observations. These models rely heavily on halo merger trees, and their realistic features (i.e., no drastic changes on halo mass or jumps on…
A central challenge in observational studies of galaxy formation is how to associate progenitor galaxies with their descendants at lower redshifts. One promising approach is to link galaxies at fixed number density, rather than fixed…
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;…