Cobaya is a modular Bayesian analysis code that exploits model interdependencies via automatic caching and a novel parameter-blocking algorithm to minimize sampling cost.
Revising the Halofit Model for the Nonlinear Matter Power Spectrum
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
Based on a suite of state-of-the-art high-resolution $N$-body simulations, we revisit the so-called halofit model (Smith et al. 2003) as an accurate fitting formula for the nonlinear matter power spectrum. While the halofit model has been frequently used as a standard cosmological tool to predict the nonlinear matter power spectrum in a universe dominated by cold dark matter, its precision has been limited by the low-resolution of $N$-body simulations used to determine the fitting parameters, suggesting the necessity of improved fitting formula at small scales for future cosmological studies. We run high-resolution $N$-body simulations for 16 cosmological models around the Wilkinson Microwave Anisotropy Probe (WMAP) best-fit cosmological parameters (1, 3, 5, and 7 year results), including dark energy models with a constant equation of state. The simulation results are used to re-calibrate the fitting parameters of the halofit model so as to reproduce small-scale power spectra of the $N$-body simulations, while keeping the precision at large scales. The revised fitting formula provides an accurate prediction of the nonlinear matter power spectrum in a wide range of wavenumber ($k \leq 30h$\,Mpc$^{-1}$) at redshifts $0 \leq z \leq 10$, with 5% precision for $k\leq1 h$ Mpc$^{-1}$ at $0 \leq z \leq 10$ and 10% for $1 \leq k\leq 10 h$ Mpc$^{-1} $ at $0 \leq z \leq 3$. We discuss the impact of the improved halofit model on weak lensing power spectra and correlation functions, and show that the improved model better reproduces ray-tracing simulation results.
representative citing papers
A Gaussian statistical model of galaxy shapes and radio polarizations yields unbiased, minimum-variance estimators for cosmic shear, intrinsic alignment, and line-of-sight rotation that are accurate to first order.
UNIONS-3500 weak lensing data yields S_8 = 0.831^{+0.067}_{-0.078} in flat LCDM from 2D cosmic shear, consistent with Planck within 1 sigma.
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
citing papers explorer
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Cobaya: Code for Bayesian Analysis of hierarchical physical models
Cobaya is a modular Bayesian analysis code that exploits model interdependencies via automatic caching and a novel parameter-blocking algorithm to minimize sampling cost.
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How to augment cosmic shear measurements with radio polarimetry of galaxies?
A Gaussian statistical model of galaxy shapes and radio polarizations yields unbiased, minimum-variance estimators for cosmic shear, intrinsic alignment, and line-of-sight rotation that are accurate to first order.
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UNIONS-3500 Weak Lensing: III. 2D Cosmological Constraints in Configuration Space
UNIONS-3500 weak lensing data yields S_8 = 0.831^{+0.067}_{-0.078} in flat LCDM from 2D cosmic shear, consistent with Planck within 1 sigma.
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.