相关论文: Rapid simulation rescaling from standard to modifi…
We develop a transformer-based conditional generative model for discrete point objects and their properties. We use it to build a model for populating cosmological simulations with gravitationally collapsed structures called dark matter…
Approximate N-body methods, such as FastPM and COLA, have been successful in modelling halo and galaxy clustering statistics, but their low resolution on small scales is a limitation for applications that require high precision. Full N-body…
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…
Future galaxy surveys require realistic mock catalogues to understand and quantify systematics in order to make precise cosmological measurements. We present a halo lightcone catalogue and halo occupation distribution (HOD) galaxy catalogue…
We study the accuracy of several approximate methods for gravitational dynamics in terms of halo power spectrum multipoles and their estimated covariance matrix. We propagate the differences in covariances into parameter constrains related…
The non-linear, scale-dependent bias in the mass distribution of galaxies and the underlying dark matter is a key systematic affecting the extraction of cosmological parameters from galaxy clustering. Using 95 million halos from the…
We present a Markov chain Monte Carlo pipeline that can be used for robust and unbiased constraints of $f(R)$ gravity using galaxy cluster number counts. This pipeline makes use of a detailed modelling of the halo mass function in $f(R)$…
The abundance of dark matter haloes is one of the key probes of the growth of structure and expansion history of the Universe. Theoretical predictions for this quantity usually assume that, when expressed in a certain form, it depends only…
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 introduce a new model of the evolution of the concentration of dark matter halos, c(t). For individual halos, our model approximates c(t) as a power law with a time-dependent index, such that at early times, concentration has a nearly…
This thesis introduces a set of methods for testing models of modified gravity using galaxy clusters. In particular, a technique for constraining models with a chameleon screening is introduced. In addition, the outlined technique is…
We introduce a new method for the identification of galaxy systems in redshift surveys based on the halo model. This method is a modified version of the K-means identification algorithm developed by Yang et al (2005). We have calibrated and…
Precision measurements of the large scale structure of the Universe require large numbers of high fidelity mock catalogs to accurately assess, and account for, the presence of systematic effects. We introduce and test a scheme for…
We review the formalism and applications of the halo-based description of nonlinear gravitational clustering. In this approach, all mass is associated with virialized dark matter halos; models of the number and spatial distribution of the…
We propose a method to generate `genetically-modified' (GM) initial conditions for high-resolution simulations of galaxy formation in a cosmological context. Building on the Hoffman-Ribak algorithm, we start from a reference simulation with…
Precision cosmology has recently triggered new attention on the topic of approximate methods for the clustering of matter on large scales, whose foundations date back to the period from late '60s to early '90s. Indeed, although the prospect…
Future galaxy surveys hope to distinguish between the dark energy and modified gravity scenarios for the accelerating expansion of the Universe using the distortion of clustering in redshift space. The aim is to model the form and size of…
We develop a machine learning approach to reconstructing the cosmological initial conditions from late-time dark matter halo number density fields in redshift space, with the goal of improving sensitivity to cosmological parameters, and in…
We apply the nonlinear reconstruction method to simulated halo fields. For halo number density $2.77\times 10^{-2}$ $(h^{-1} {\rm Mpc})^{-3}$ at $z=0$, corresponding to the SDSS main sample density, we find the scale where the noise…
We recently developed a generalization of the halo model in order to describe the spatial clustering properties of each mass component in the Universe, including hot gas and stars. In this work we discuss the complementarity of the model…