Related papers: Machine Learning and Cosmological Simulations II: …
In the era of precision cosmology, the ability to generate accurate and large-scale galaxy catalogs is crucial for advancing our understanding of the universe. With the flood of cosmological data from current and upcoming missions,…
Disentangling the stellar population in the central galaxy from the intrahalo light can help us shed light on the formation history of the host halo, as the properties of the stellar components are expected to retain traces of its formation…
Context:Halo formation time, which quantifies the mass assembly history of dark-matter halos, directly impacts galaxy properties and evolution. Although not directly observable, it can be inferred through proxies like star formation history…
The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or…
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…
Hydrodynamical shocks are a manifestation of the non-linearity of the Euler equations and play a fundamental role in cosmological gas dynamics. In this work, we identify and analyse shocks in the Illustris simulation, and contrast the…
We review the progress in modelling the galaxy population in hydrodynamical simulations of the Lambda-CDM cosmogony. State-of-the-art simulations now broadly reproduce the observed spatial clustering of galaxies, the distributions of key…
We recently developed a generalization of the halo model in order to describe the spatial clustering properties of each mass component in the Universe, including hot gas and stars. In this work we discuss the complementarity of the model…
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 $\rm{M}_{\rm{halo}}$…
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…
Using a large sample of galaxies taken from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, a suite of hydrodynamic simulations varying both cosmological and astrophysical parameters, we train a…
Halo models provide a simple and computationally inexpensive way to investigate the connection between galaxies and their dark matter haloes. However, these models rely on the assumption that the role of baryons can be easily parametrized…
We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…
The current status of numerical simulations of galaxy formation is reviewed. After a short description of the main numerical simulation techniques, three sample applications illustrate how numerical simulations have provided deeper insight…
To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and halos accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the…
Hydrodynamical cosmological simulations are a powerful tool for accurately predicting the properties of the intergalactic medium (IGM) and for producing mock skies that can be compared against observational data. However, the need to…
We use the IllustrisTNG50 cosmological hydrodynamical simulation, complemented by a catalog of tagged globular clusters, to investigate the properties and build up of two extended luminous components: the intra-cluster light (ICL) and the…
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…
Machine learning (ML) is often viewed as a black-box regression technique that is unable to provide considerable scientific insight. ML models are universal function approximators and - if used correctly - can provide scientific information…
We perform an extensive analysis of nonlinear and stochastic biasing of galaxies and dark halos in spatially flat low-density CDM universe using cosmological hydrodynamic simulations. We compare their biasing properties with the predictions…