Related papers: A graph-based Neural Network surrogate model for a…
Attenuation of high--energy gamma rays by pair--production with UV, optical and IR background photons provides a link between the history of galaxy formation and high--energy astrophysics. We present results from our latest semi-analytic…
We have constructed merger trees for galaxies in the Illustris Simulation by directly tracking the baryonic content of subhalos. These merger trees are used to calculate the galaxy-galaxy merger rate as a function of descendant stellar…
Galaxy formation, viewed as an inference problem from incomplete information, is inherently stochastic. Reducing the full simulation state to a coarse-grained set of variables integrates out unresolved degrees of freedom, motivating an…
We develop a new formalism for modeling the formation and evolution of galaxies within a hierarchical universe. Similarly to standard semi-analytical models we trace galaxies inside dark-matter merger-trees. The formalism includes treatment…
We propose GNN-Surrogate, a graph neural network-based surrogate model to explore the parameter space of ocean climate simulations. Parameter space exploration is important for domain scientists to understand the influence of input…
Graphs are naturally used to describe the structures of various real-world systems in biology, society, computer science etc., where subgraphs or motifs as basic blocks play an important role in function expression and information…
We study the impact of star formation and stellar feedback prescriptions on galaxy properties predicted by means of "stripped-down" versions of independently developed semi-analytic models (SAMs). These include cooling, star formation,…
Rapidly growing catalogs of compact binary mergers from advanced gravitational-wave detectors allow us to explore the astrophysics of massive stellar binaries. Merger observations can constrain the uncertain parameters that describe the…
We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic…
We construct the Numerical Galaxy Catalog ($\nu$GC), based on a semi-analytic model of galaxy formation combined with high-resolution N-body simulations in a $\Lambda$-dominated flat cold dark matter ($\Lambda$CDM) cosmological model. The…
In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, deep learning techniques have increasingly been utilized to…
With the advent of several galaxy surveys targeting star-forming galaxies, it is important to have models capable of interpreting their spatial distribution in terms of astrophysical and cosmological parameters. To address this need, we…
Graph Neural Networks (GNNs) have shown remarkable success in graph classification tasks by capturing both structural and feature-based representations. However, real-world graphs often exhibit two critical forms of imbalance: class…
Among the possible alternatives to the standard cosmological model ($\Lambda$CDM), coupled Dark Energy models postulate that Dark Energy (DE), seen as a dynamical scalar field, may interact with Dark Matter (DM), giving rise to a…
Semi-analytic models (SAMs) are currently one of the primary tools with which to model statistically significant ensembles of galaxies. The underlying physical prescriptions inherent to each SAM are, in many cases, different from one…
Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO$_2$ geological storage. Accurately capturing the impact of faults on CO$_2$ plume migration remains a…
We study the metallicities and abundance ratios of early-type galaxies in cosmological semi-analytic models (SAMs) within the hierarchical galaxy formation paradigm. To achieve this we implemented a detailed galactic chemical evolution…
Accurate yet efficient surrogate models are essential for large-scale simulations of partial differential equations (PDEs), particularly for uncertainty quantification (UQ) tasks that demand hundreds or thousands of evaluations. We develop…
We study the behaviour of multiple radiative cooling algorithms implemented in seven Semi-Analytic Models (SAMs) of galaxy formation, including a new model we propose in this paper. We use versions of the models without feedback and apply…
We developed a new semi-analytic galaxy formation model: Galaxy Assembly with Binary Evolution (GABE). For the first time we introduce binary evolution into semi-analytic models of galaxy formation by using Yunnan-II stellar population…