Neighbor-density-weighted marked correlation functions improve FoM for Ωm–σ8 by 1.7–2.5× over standard 2PCF using Gaussian-process emulators on 129 w0waCDM+∑mν simulations.
The Coyote Universe I: Precision Determination of the Nonlinear Matter Power Spectrum
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abstract
Near-future cosmological observations targeted at investigations of dark energy pose stringent requirements on the accuracy of theoretical predictions for the clustering of matter. Currently, N-body simulations comprise the only viable approach to this problem. In this paper we demonstrate that N-body simulations can indeed be sufficiently controlled to fulfill these requirements for the needs of ongoing and near-future weak lensing surveys. By performing a large suite of cosmological simulation comparison and convergence tests we show that results for the nonlinear matter power spectrum can be obtained at 1% accuracy out to k~1 h/Mpc. The key components of these high accuracy simulations are: precise initial conditions, very large simulation volumes, sufficient mass resolution, and accurate time stepping. This paper is the first in a series of three, with the final aim to provide a high-accuracy prediction scheme for the nonlinear matter power spectrum.
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Bayesian hierarchical modeling of photometric redshifts in KiDS+VIKING-450 raises S8 to 0.756 ± 0.039 and reduces Planck tension to 1.9σ.
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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Cosmological constraints from neighbor-density-weighted marked correlation functions
Neighbor-density-weighted marked correlation functions improve FoM for Ωm–σ8 by 1.7–2.5× over standard 2PCF using Gaussian-process emulators on 129 w0waCDM+∑mν simulations.
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KiDS+VIKING-450 cosmology with Bayesian hierarchical model redshift distributions
Bayesian hierarchical modeling of photometric redshifts in KiDS+VIKING-450 raises S8 to 0.756 ± 0.039 and reduces Planck tension to 1.9σ.
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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.