Related papers: A graph-based Neural Network surrogate model for a…
As urban environments grow, the modelling of transportation systems becomes increasingly complex. This paper advances the field of travel demand modelling by introducing advanced Graph Neural Network (GNN) architectures as surrogate models,…
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms -- label propagation and graph neural networks -- both operate by repeatedly passing information along edges,…
Weak gravitational lensing maps compactly encode the evolution of cosmic large-scale structure and are a key tool for cosmological analyses. Performing inference directly at the map level allows flexible choices of statistics and can…
Upcoming cosmological surveys will provide unprecedented amount of data, which will require innovative statistical methods to maximize the scientific exploitation. Standard cosmological analyses based on abundances, two-point and…
The formation and evolution of galaxies can be followed in the context of cosmological structure formation using the technique of semi-analytic modelling. We give a brief outline of the features incorporated into the semi-analytic model of…
In genome-scale constraint-based metabolic models, gene deletion strategies are essential for achieving growth-coupled production, where cell growth and target metabolite synthesis occur simultaneously. Despite the inherently networked…
The evolution of the galaxy stellar mass function is especially useful to test the current model of galaxy formation. Observational data have revealed a few inconsistencies with predictions from the $\Lambda {\rm CDM}$ model. For example,…
Machine learning and artificial neural networks (ANNs) have increasingly become integral to data analysis research in astrophysics due to the growing demand for fast calculations resulting from the abundance of observational data.…
We introduce methods which allow observed galaxy clustering to be used together with observed luminosity or stellar mass functions to constrain the physics of galaxy formation. We show how the projected two-point correlation function of…
We have developed a new semi-analytic model for the formation and evolution of structure on galaxy, group and cluster scales. The model combines merger trees with a detailed, spatially resolved description of the dynamical evolution of halo…
We introduce a mock galaxy catalog built for the CSST extragalactic surveys using the primary runs of the Jiutian $N$-body simulation suites. The catalogs are built by coupling the GAlaxy Evolution and Assembly (GAEA) semi-analytical model…
We present the novel wide & deep neural network GalaxyNet, which connects the properties of galaxies and dark matter haloes, and is directly trained on observed galaxy statistics using reinforcement learning. The most important halo…
Based on the star formation histories (SFH) of galaxies in halos of different masses, we develop an empirical model to grow galaxies in dark mattet halos. This model has very few ingredients, any of which can be associated to observational…
Recent observations of the distant Universe suggest that much of the stellar mass of bright galaxies was already in place at $z>1$. This presents a challenge for models of galaxy formation because massive halos are assembled late in…
Galaxy formation models, particularly semi-analytic models (SAMs), rely on differential equations with free parameters to describe the physical mechanisms governing galaxy formation and evolution. Traditionally, most SAMs calibrate these…
We present a new evolutionary model for the far-UV to sub-mm properties of the galaxy population. This combines a semi-analytic galaxy formation model based on hierarchical clustering (GALFORM) with a spectro-photometric code which includes…
We directly compare predictions of dwarf galaxy properties in a semi-analytic model (SAM) with those extracted from a high-resolution hydrodynamic simulation. We focus on galaxies with halo masses of 1e9<Mvir/Msol<1e11 at high redshift…
As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing…
We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional inputs, and generates…
Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operate in static settings…