相关论文: Secondary halo bias through cosmic time II: Recons…
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…
Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the…
Reliable extraction of cosmological information from clustering measurements of galaxy surveys requires estimation of the error covariance matrices of observables. The accuracy of covariance matrices is limited by our ability to generate…
We measure the signal of secondary halo bias as a function of a variety of intrinsic and environmental halo properties, and characterize its statistical significance as a function of cosmological redshift. Using fixed and paired $N$-body…
Halo bias links the statistical properties of the spatial distribution of dark matter halos to those of the underlying dark matter field, providing insights into clustering properties in both general relativity (GR) and modified-gravity…
Plenty of crucial information about our Universe is encoded in the cosmic large-scale structure (LSS). However, the extractions of these information are usually hindered by the nonlinearities of the LSS, which can be largely alleviated by…
Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes' assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a…
The clustering of dark matter halos depends not only on their mass, the so-called primary bias, but also on their internal properties, the so-called secondary bias. While the former effect is well-understood within the Press-Schechter (PS)…
Over $90$\% of dark matter haloes in cosmological simulations have unresolved properties. This can hinder the dynamical range of simulations and result in systematic biases when modelling cosmological tracers. We aim to more precisely…
High-resolution cosmological N-body simulations are excellent tools for modelling the formation and clustering of dark matter haloes. These simulations suggest complex physical theories of halo formation governed by a set of effective…
The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex…
Shape estimates that quantify the halo anisotropic mass distribution are valuable parameters that provide information on their assembly process and evolution. Measurements of the mean shapes for a sample of cluster-sized halos can be used…
Secondary halo bias, commonly known as 'assembly bias,' is the dependence of halo clustering on a halo property other than mass. This prediction of the Lambda-Cold Dark Matter cosmology is essential to modelling the galaxy distribution to…
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…
The structural and dynamic properties of the dark matter halos, though an important ingredient in understanding large-scale structure formation, require more conservative particle resolution than those required by halo mass alone in a…
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…
We explore the phenomenon commonly known as halo assembly bias, whereby dark matter halos of the same mass are found to be more or less clustered when a second halo property is considered, for halos in the mass range $3.7 \times 10^{11} \;…
Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very…
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 present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo…