Related papers: Predicting halo occupation and galaxy assembly bia…
We present a clustering analysis of ~60,000 massive (stellar mass Mstar > 10^{11} Msun) galaxies out to z = 1 drawn from 55.2 deg2 of the UKIRT Infrared Deep Sky Survey (UKIDSS) and the Sloan Digital Sky Survey (SDSS) II Supernova Survey.…
The spatial distribution of massive and luminous galaxies have provided important constraints on the fundamental cosmological parameters and physical processes governing galaxy formation. In this work, we construct and compare independent…
We develop a machine learning (ML) framework to populate large dark matter-only simulations with baryonic galaxies. Our ML framework takes input halo properties including halo mass, environment, spin, and recent growth history, and outputs…
We present a method that uses observations of galaxies to simultaneously constrain cosmological parameters and the galaxy-dark matter connection (aka halo occupation statistics). The latter describes how galaxies are distributed over dark…
We present evidence for halo assembly bias as a function of geometric environment. By classifying GAMA galaxy groups as residing in voids, sheets, filaments or knots using a tidal tensor method, we find that low-mass haloes that reside in…
Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center. Departure of the…
We simultaneously constrain cosmology and galaxy bias using measurements of galaxy abundances, galaxy clustering and galaxy-galaxy lensing taken from the Sloan Digital Sky Survey. We use the conditional luminosity function (which describes…
The next generation of galaxy surveys will provide more precise measurements of galaxy clustering than have previously been possible. The 21-cm radio signals that are emitted from neutral atomic hydrogen (HI) gas will be detected by…
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 quantify the accuracy with which the cosmological parameters characterizing the energy density of matter (\Omega_m), the amplitude of the power spectrum of matter fluctuations (\sigma_8), the energy density of neutrinos (\Omega_{\nu})…
One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is…
The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable machine-learning framework to provide physical insights into…
The clustering of matter on cosmological scales is an essential probe for studying the physical origin and composition of our Universe. To date, most of the direct studies have focused on shear-shear weak lensing correlations, but it is…
Large surveys of the local Universe have shown that galaxies with different intrinsic properties, such as colour, luminosity and morphological type display a range of clustering amplitudes. Galaxies are therefore not faithful tracers of the…
We investigate galaxy bias in the framework of the ``Halo Occupation Distribution'' (HOD), which defines the bias of a population of galaxies by the conditional probability P(N|M) that a dark matter halo of virial mass M contains N…
We present observational constraints on the galaxy-halo connection, focusing particularly on galaxy assembly bias, from a novel combination of counts-in-cylinders statistics, $P(N_{\rm{CIC}})$, with the standard measurements of the…
We present an end-to-end description of the formation process of globular clusters (GCs) which combines a treatment for their formation and dynamical evolution within galaxy haloes with a state-of-the-art semi-analytic simulation of galaxy…
The halo model provides a powerful framework for interpreting galaxy clustering by linking the spatial distribution of dark matter haloes to the underlying matter distribution. A key assumption within the halo bias approximation of the halo…
We build a deep learning framework that connects the local formation process of dark matter halos to the halo bias. We train a convolutional neural network (CNN) to predict the final mass and concentration of dark matter halos from the…
We develop a novel method to explore the galaxy-halo connection using the galaxy imaging surveys by modeling the projected two-point correlation function measured from the galaxies with reasonable photometric redshift measurements. By…