Related papers: Halo assembly bias from a deep learning model of h…
We present a generalization of our recently proposed machine learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the…
We study halo assembly bias for cluster-sized halos. Previous work has found little evidence for correlations between large-scale bias and halo mass assembly history for simulated cluster-sized halos, in contrast to the significant…
When constructing galaxy mock catalogs based on suits of dark matter halo catalogs generated with approximated, calibrated or machine-learning approaches, the assignment of intrinsic properties for such tracers is a step of paramount…
We investigate the clustering of dark matter halos in Lagrangian space in terms of their two-point correlation function, spanning more than 4 orders of magnitudes of halo masses. Analyzing a set of collisionless scale-free 128^3-particle…
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…
We use an extremely large volume ($2.4h^{-3}{\rm Gpc}^{3}$), high resolution N-body simulation to measure the higher order clustering of dark matter haloes as a function of mass and internal structure. As a result of the large simulation…
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…
The halo assembly bias, a phenomenon referring to dependencies of the large-scale bias of a dark matter halo other than its mass, is a fundamental property of the standard cosmological model. First discovered in 2005 from the Millennium Run…
The properties of the matter density field in the initial conditions have a decisive impact on the features of the large-scale structure of the Universe as observed today. These need to be studied via $N$-body simulations, which are…
Associating the formation sites of haloes with the maxima of the smoothed linear density field, we present non-perturbative predictions for the Lagrangian and evolved halo correlation functions that are valid at all separations. In…
We build a simple analytical model for the bias of dark matter halos that applies to objects defined by an arbitrary density threshold, $200\leq\deltas\leq 1600$, and that provides accurate predictions from low-mass to high-mass halos. We…
Understanding the impact of environment on the formation and evolution of dark matter halos and galaxies is a crucial open problem. Studying statistical correlations in large simulated populations sheds some light on these impacts, but the…
The two-point clustering of dark matter halos is influenced by halo properties besides mass, a phenomenon referred to as halo assembly bias. Using the depth of the gravitational potential well, $V_{\rm max}$, as our secondary halo property,…
For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations. Instead…
We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce…
We measure the clustering of dark matter halos in a large set of collisionless cosmological simulations of the flat LCDM cosmology. Halos are identified using the spherical overdensity algorithm, which finds the mass around isolated peaks…
In this paper we use the ``Millennium Simulation'' to re-examine the mass assembly history of dark matter halos and the age dependence of halo clustering. We use eight different definitions of halo formation times to characterize the…
Large-scale sky surveys require companion large volume simulated mock catalogs. To ensure precision cosmology studies are unbiased, the correlations in these mocks between galaxy properties and their large-scale environments must be…
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 clustering of dark matter haloes with fixed mass depends on their formation history, an effect known as assembly bias. We use zoom N -body simulations to investigate the origin of this phenomenon. For each halo at redshift z=0, we…