Related papers: Predicting galaxy bias using machine learning
We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field. By fitting the mass-weighted halo…
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky…
In addition to the intrinsic clustering of galaxies themselves, the spatial distribution of galaxies observed in surveys is modulated by the presence of weak lensing due to matter in the foreground. This effect, known as magnification bias,…
We study the impact that uncertainties on assumed relations between galaxy bias parameters have on constraints of the local PNG $f_{\rm NL}$ parameter. We focus on the relation between the linear density galaxy bias $b_1$ and local PNG bias…
The galaxy-galaxy lensing technique allows us to measure the subhalo mass of satellite galaxies, studying their mass loss and evolution within galaxy clusters and providing direct observational validation for theories of galaxy formation.…
Metacalibration is a new technique for measuring weak gravitational lensing shear that is unbiased for isolated galaxy images. In this work we test metacalibration with overlapping, or ``blended'' galaxy images. Using standard…
Assembly bias is the variation in the clustering of dark matter halos and galaxies that arises from correlations between the halo assembly history and the large-scale environment at fixed halo mass. In this work, we use the cosmological…
We use the Millennium Simulation, a 10 billion particle simulation of the growth of cosmic structure, to construct a new model of galaxy clustering. We adopt a methodology that falls midway between the traditional semi-analytic approach and…
We use hydrodynamical separate universe simulations with the IllustrisTNG model to predict the local primordial non-Gaussianity (PNG) bias parameters $b_{\phi}$ and $b_{\phi\delta}$, which enter at leading order in the galaxy power spectrum…
Orbit superposition models are a non-parametric dynamical modelling technique to determine the mass of a galaxy's central supermassive black hole (SMBH), its stars, or its dark-matter halo. One of the main problems is how to decide which…
We examine the influence of the morphology-density(MD) relation and a wide range of simple models for biased galaxy formation on statistical measures of large scale structure. We contrast the behavior of local biasing models, in which the…
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…
Observed kinematical data of 40 Local Group (LG) members are used to derive the dark matter halo mass of such galaxies. Haloes are selected from the theoretically expected LG mass function and two different density profiles are assumed, a…
We use semi-analytic models of galaxy formation combined with high resolution N-body simulations to make predictions for galaxy-dark matter correlations and apply them to galaxy-galaxy lensing. We analyze cross-correlation spectra between…
This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with absolute…
We present a new approach for modelling galaxy/halo bias that utilizes the full non-linear information contained in the moments of the matter density field, which we derive using a set of numerical simulations. Although our method is…
We apply a new method to measure primordial non-Gaussianity, using the cross-correlation between galaxy surveys and the CMB lensing signal to measure galaxy bias on very large scales, where local-type primordial non-Gaussianity predicts a…
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…
We present an analysis of the quenching of star formation in galaxies, bulges, and disks throughout the bulk of cosmic history, from $z=2-0$. We utilise observations from the SDSS and MaNGA at low redshifts. We complement these data with…
We use galaxy groups selected from the Sloan Digital Sky Survey (SDSS) together with mass models for individual groups to study the galaxy-galaxy lensing signals expected from galaxies of different luminosities and morphological types. We…