相关论文: Learning the galaxy-environment connection with gr…
We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study…
We study the co-evolution of dark matter halos, galaxies and supermassive black holes using an empirical galaxy evolution model from $z=0$ -- $10$. We demonstrate that by connecting dark matter structure evolution with simple empirical…
We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. As an example, we use the IllustrisTNG hydrodynamic…
We provide new constraints on the connection between galaxies in the local universe, identified by the Sloan Digital Sky Survey (SDSS), and dark matter halos and their constituent substructures in the $\Lambda$CDM model using WMAP7…
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,…
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…
Forthcoming cosmological imaging surveys, such as the Rubin Observatory LSST, require large-scale simulations encompassing realistic galaxy populations for a variety of scientific applications. Of particular concern is the phenomenon of…
We use explainable neural networks to connect the evolutionary history of dark matter halos with their density profiles. The network captures independent factors of variation in the density profiles within a low-dimensional representation,…
The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here we present a machine…
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…
In this paper we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter only…
We use TNG and EAGLE hydrodynamic simulations to investigate the central galaxy - dark matter halo relations that are needed for a halo-based empirical model of star formation in galaxies. Using a linear dimension reduction algorithm and a…
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally inferred SHMFs to theoretical predictions remains challenging, as…
We present a novel method to infer the Dark Matter (DM) content and spatial distribution within galaxies, based on convolutional neural networks trained within state-of-the-art hydrodynamical simulations (Illustris TNG100). The framework we…
We investigate how galactic disk structures connect to the detailed properties of their host dark-matter halos using the TNG50 simulation. From the hydrodynamic and matched dark-matter-only runs, we measure a comprehensive list of halo…
The information extracted from large galaxy surveys with the likes of DES, DESI, Euclid, LSST, SKA, and WFIRST will be greatly enhanced if the resultant galaxy catalogues can be cross-correlated with one another. Predicting the nature of…
Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller…
The role of baryonic physics, star formation, and stellar feedback, in shaping the galaxies and their host halos is an evolving topic. The dark matter aspects are illustrated in this work by showing distribution features in a…
We introduce a novel halo/galaxy matching technique between two cosmological simulations with different resolutions, which utilizes the positions and masses of halos along their subhalo merger tree. With this tool, we conduct a study of…
The scaling relations between supermassive black holes and their host galaxy properties are of fundamental importance in the context black hole-host galaxy co-evolution throughout cosmic time. In this work, we use a novel algorithm that…