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相关论文: Generating Cosmological Gaussian Random Fields

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The conventional method of generating initial conditions for cosmological N-body simulations introduces a significant error in the real-space statistical properties of the particles. More specifically, the finite box size leads to a…

天体物理学 · 物理学 2009-11-10 Edwin Sirko

We discuss a new algorithm to generate multi-scale initial conditions with multiple levels of refinements for cosmological "zoom-in" simulations. The method uses an adaptive convolution of Gaussian white noise with a real space transfer…

宇宙学与河外天体物理 · 物理学 2015-05-27 Oliver Hahn , Tom Abel

The statistical translation invariance of cosmological random fields is broken by a finite survey boundary, correlating the observable Fourier modes. Standard methods for generating Gaussian fields either neglect these correlations, or are…

宇宙学与河外天体物理 · 物理学 2015-06-22 Julien Carron , Melody Wolk , Istvan Szapudi

We describe how to define an extremely large discrete realisation of a Gaussian white noise field that has a hierarchical structure and the property that the value of any part of the field can be computed quickly. Tiny subregions of such a…

宇宙学与河外天体物理 · 物理学 2015-06-16 Adrian Jenkins

This paper describes the generation of initial conditions for numerical simulations in cosmology with multiple levels of resolution, or multiscale simulations. We present the theory of adaptive mesh refinement of Gaussian random fields…

天体物理学 · 物理学 2009-07-09 Edmund Bertschinger

We investigate the possibility of generating initial conditions for cosmological N-body simulations by simulating a system whose correlations at thermal equilibrium approximate well those of cosmological density perturbations. The system is…

天体物理学 · 物理学 2009-11-10 M. Joyce , D. Levesque , B. Marcos

We address the generation of initial conditions (ICs) for GRAMSES, a code for nonlinear general relativistic (GR) $N$-body cosmological simulations recently introduced in Ref. [1]. GRAMSES adopts a constant mean curvature slicing with a…

宇宙学与河外天体物理 · 物理学 2020-05-06 Cristian Barrera-Hinojosa , Baojiu Li

I present a new approach to recover the primordial density fluctuations and the cosmic web structure underlying a galaxy distribution. The method is based on sampling Gaussian fields which are compatible with a galaxy distribution and a…

宇宙学与河外天体物理 · 物理学 2015-06-04 Francisco-Shu Kitaura

We describe cosmological simulation techniques and their application to studies of cosmic structure formation, with particular attention to recent hydrodynamic simulations of structure in the high redshift universe. Collisionless N-body…

天体物理学 · 物理学 2008-02-03 David H. Weinberg , Neal Katz , Lars Hernquist

Reconstructing the initial conditions of the universe is a key problem in cosmology. Methods based on simulating the forward evolution of the universe have provided a way to infer initial conditions consistent with present-day observations.…

宇宙学与河外天体物理 · 物理学 2023-04-11 Ronan Legin , Matthew Ho , Pablo Lemos , Laurence Perreault-Levasseur , Shirley Ho , Yashar Hezaveh , Benjamin Wandelt

Simulating sample correlation matrices is important in many areas of statistics. Approaches such as generating Gaussian data and finding their sample correlation matrix or generating random uniform $[-1,1]$ deviates as pairwise correlations…

统计理论 · 数学 2013-12-09 Johanna Hardin , Stephan Ramon Garcia , David Golan

This document describes analytical and numerical techniques for the generation of Gaussian density fields, which represent cosmological density perturbations. The mathematical techniques involved in the generation of density harmonics in…

天体物理学 · 物理学 2007-05-23 Hugo Martel

Non-Gaussianity in the cosmic microwave background and the large-scale structure of galaxies provides an increasingly powerful probe of the universe. I implement an algorithm to generate realisations of fields that possess an arbitrary…

宇宙学与河外天体物理 · 物理学 2015-06-15 Iain A. Brown

We make a very large realisation of a Gaussian white noise field, called PANPHASIA, public by releasing software that computes this field. Panphasia is designed specifically for setting up Gaussian initial conditions for cosmological…

天体物理仪器与方法 · 物理学 2013-06-26 Adrian Jenkins , Stephen Booth

We computed the power spectrum of weak cosmic shear in models with non-Gaussian primordial density fluctuations. Cosmological initial conditions deviating from Gaussianity have recently attracted much attention in the literature, especially…

宇宙学与河外天体物理 · 物理学 2015-05-14 C. Fedeli , L. Moscardini

We extend field-level inference to jointly constrain the cosmological parameters $\{A,\omega_{\rm cdm},H_0\}$, in both real and redshift space. Our analyses are based on mock data generated using a perturbative forward model, with noise…

宇宙学与河外天体物理 · 物理学 2025-09-25 Kazuyuki Akitsu , Marko Simonović , Shi-Fan Chen , Giovanni Cabass , Matias Zaldarriaga

We present the study of ten random realizations of a density field characterized by a cosmological power spectrum P(k) at redshift z=50. The reliability of such initial conditions for n-body simulations are tested with respect to their…

天体物理学 · 物理学 2009-11-07 Alexander Knebe , Alvaro Dominguez

We present a method for customizing the root grid of zoom-in initial conditions used for simulations of galaxy formation. Starting from the white noise used to seed the structures of an existing initial condition, we cut out a smaller…

星系天体物理 · 物理学 2020-09-22 Gillen Brown , Oleg Y. Gnedin

Conventional cosmological initial condition generators are designed exclusively for fully periodic cubic domains and cannot produce the non-periodic, observer-centric configurations required by stereographically projected N-body codes such…

宇宙学与河外天体物理 · 物理学 2026-05-12 Balázs Pál , Gábor Rácz , István Csabai , István Szapudi

This technical paper describes a software package that was designed to produce initial conditions for large cosmological simulations in the context of the Horizon collaboration. These tools generalize E. Bertschinger's Grafic1 software to…

天体物理学 · 物理学 2009-11-13 S. Prunet , C. Pichon , D. Aubert , D. Pogosyan , R. Teyssier , S. Gottloeber
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