Related papers: Learning the galaxy-environment connection with gr…
Determining the dynamical mass profiles of dispersion-supported galaxies is particularly challenging due to projection effects and the unknown shape of their velocity anisotropy profile. Our goal is to develop a machine learning algorithm…
More precise measurements of galaxy clustering will be provided by the next generation of galaxy surveys such as DESI, WALLABY and SKA. To utilize this information to improve our understanding of the Universe, we need to accurately model…
The simplest scheme for predicting real galaxy properties after performing a dark matter simulation is to rank order the real systems by stellar mass and the simulated systems by halo mass and then simply assume monotonicity - that the more…
We evaluate the ability of Convolutional Neural Networks (CNNs) to predict galaxy cluster masses in the BAHAMAS hydrodynamical simulations. We train four separate single-channel networks using: stellar mass, soft X-ray flux, bolometric…
The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a…
We investigate the impact of the number of filaments connected to the nodes of the cosmic web on the physical properties of their galaxies using the Sloan Digital Sky Survey. We compare these measurements to the cosmological hydrodynamical…
Empirical models of galaxy formation require assumptions about the correlations between galaxy and halo properties. These may be calibrated against observations or inferred from physical models such as hydrodynamical simulations. In this…
We present a maximum-likelihood analysis of galaxy-galaxy lensing effects in galaxy clusters and in the field. The aim is to determine the accuracy and robustness of constraints that can be obtained on galaxy halo properties in both…
Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-halos or due to physical mechanisms affecting the baryons throughout galaxy evolution.…
We study the evolution of baryonic gas in cosmologically growing dark matter halos. To accurately model both the inner and outer regions of the halos, we use a dark matter density profile that transitions smoothly from the NFW profile…
Understanding how galaxy properties are linked to the dark matter halos they reside in, and how they co-evolve is a powerful tool to constrain the processes related to galaxy formation. The stellar-to-halo mass relation (SHMR) and its…
The halo mass function, dn/dM, predicted by hierarchical clustering models can be measured indirectly using dynamical probes like the distribution of gravitational lens image separations, dn/dDelta-theta, or halo circular velocities, dn/d…
An ambitious goal in cosmology is to forward-model the observed distribution of galaxies in the nearby Universe today from the initial conditions of large-scale structures. For practical reasons, the spatial resolution at which this can be…
We introduce a simple analytic model of galaxy formation that links the growth of dark matter haloes in a cosmological background to the build-up of stellar mass within them. The model aims to identify the physical processes that drive the…
Using a novel machine learning method, we investigate the buildup of galaxy properties in different simulations, and in various environments within a single simulation. The aim of this work is to show the power of this approach at…
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms. Although spectroscopy provides a rich description of the physical processes that govern galaxy…
The formation of the disk and feedback from supernova winds impacts the distribution of dark matter in galaxies. Recently, Di Cintio et al. (2014b) characterized the halo response from baryonic processes in hydrodynamical simulations via a…
In this work, we seek to improve the velocity reconstruction of clusters by using Graph Neural Networks -- a type of deep neural network designed to analyze sparse, unstructured data. In comparison to the Convolutional Neural Network (CNN)…
We develop a theoretical framework that combines measurements of galaxy-galaxy lensing, galaxy clustering, and the galaxy stellar mass function in a self-consistent manner. While considerable effort has been invested in exploring each of…
The study of dark matter substructure through strong gravitational lensing has shown enormous promise in probing the properties of dark matter on sub-galactic scales. This approach has already been used to place strong constraints on a wide…