Related papers: A deep learning approach to halo merger tree const…
Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes' assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a…
We present a comparison of the statistical properties of dark matter halo merger trees extracted from the Millennium Simulation with Extended Press-Schechter (EPS) formalism and the related GALFORM Monte-Carlo method for generating…
Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However,…
We present a detailed comparison between numerical cosmological hydrodynamic zoom simulations and semi-analytic models (SAMs) run within merger trees extracted from the simulations. The high-resolution simulations represent 48 individual…
We present the public data release of halo and galaxy catalogues extracted from the EAGLE suite of cosmological hydrodynamical simulations of galaxy formation. These simulations were performed with an enhanced version of the GADGET code…
We use sparse regression methods (SRM) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter halos. SRM are machine learning algorithms…
Deep clustering (DC) leverages the representation power of deep architectures to learn embedding spaces that are optimal for cluster analysis. This approach filters out low-level information irrelevant for clustering and has proven…
In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the Universe with realistic galaxy populations are required. In particular, the tendency of galaxies to naturally align towards overdensities, an…
The analysis of cosmological galaxy surveys requires realistic simulations for their interpretation. Forward modelling is a powerful method to simulate galaxy clustering without the need for an underlying complex model. This approach…
According to the lambda CDM scenario, galaxies are formed through the hierarchical accretion of building blocks. Our Galaxy is a privileged place to look for the remnants of accretion events through the study of the chemical and kinematic…
We examine the influence of dark matter halo assembly on the evolution of a simulated $\sim L^\star$ galaxy. Starting from a zoom-in simulation of a star-forming galaxy evolved with the EAGLE galaxy formation model, we use the genetic…
We develop a galaxy assignment scheme that populates dark matter halos with galaxies by tracing the most bound member particles (MBPs) of simulated halos. Several merger-timescale models based on analytic calculations and numerical…
We use N-body cosmological simulations and empirical galaxy models to study the merger history of dwarf-mass galaxies (with M_halo~10^10 M_Sun). Our input galaxy models describe the stellar mass-halo mass relation, and the galaxy occupation…
Dark matter in the universe evolves through gravity to form a complex network of halos, filaments, sheets and voids, that is known as the cosmic web. Computational models of the underlying physical processes, such as classical N-body…
High costs and uncertainties make subsurface decision-making challenging, as acquiring new data is rarely scalable. Embedding geological knowledge directly into predictive models offers a valuable alternative. A joint approach enables just…
Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point…
Generative adversarial networks (GANs) have been recently applied as a novel emulation technique for large scale structure simulations. Recent results show that GANs can be used as a fast, efficient and computationally cheap emulator for…
We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical…
We present a semi-analytic model of satellite galaxies, SatGen, which can generate large samples of satellite populations for a host halo of desired mass, redshift, and assembly history. The model combines dark-matter halo merger trees,…
For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or non-luminous baryons, from observed luminous tracers. The dominant approach in cosmology has been to use the…