Derives analytical solutions and fitting formulae for Lyα spectra under cylindrical geometry including recoil and velocity gradients, validated against Monte Carlo simulations.
The Coyote Universe II: Cosmological Models and Precision Emulation of the Nonlinear Matter Power Spectrum
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abstract
The power spectrum of density fluctuations is a foundational source of cosmological information. Precision cosmological probes targeted primarily at investigations of dark energy require accurate theoretical determinations of the power spectrum in the nonlinear regime. To exploit the observational power of future cosmological surveys, accuracy demands on the theory are at the one percent level or better. Numerical simulations are currently the only way to produce sufficiently error-controlled predictions for the power spectrum. The very high computational cost of (precision) N-body simulations is a major obstacle to obtaining predictions in the nonlinear regime, while scanning over cosmological parameters. Near-future observations, however, are likely to provide a meaningful constraint only on constant dark energy equation of state 'wCDM' cosmologies. In this paper we demonstrate that a limited set of only 37 cosmological models -- the "Coyote Universe" suite -- can be used to predict the nonlinear matter power spectrum at the required accuracy over a prior parameter range set by cosmic microwave background observations. This paper is the second in a series of three, with the final aim to provide a high-accuracy prediction scheme for the nonlinear matter power spectrum for wCDM cosmologies.
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2026 8representative citing papers
21cmEMUv3 emulates the cylindrical 21cm power spectrum via score-based diffusion and six other 21cmFAST observables via LSTM networks at sub-percent accuracy, then uses the emulator to infer a lower limit on soft-band X-ray luminosity from HERA data.
Calibrates a one-parameter semi-analytic damping model for oscillatory primordial power spectrum features using N-body simulations and validates sub-percent accuracy via GPR emulation when modulation frequency is high enough.
AI trained on LambdaCDM simulations reconstructs the cosmic vorticity field from SDSS galaxies, revealing coherent vortical structures consistent with the standard model and correcting redshift-space distortions.
Neighbor-density-weighted marked correlation functions improve FoM for Ω_m–σ_8 by 1.7–2.5× over standard 2PCF using emulators from 129 w0waCDM+∑m_ν simulations.
Bayesian hierarchical modeling of photometric redshifts in KiDS+VIKING-450 raises S8 to 0.756 ± 0.039 and reduces Planck tension to 1.9σ.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
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Analytical and fitting formulae for solutions to Lyman-alpha radiative transfer equations: the effects of geometry, recoil, and velocity gradients
Derives analytical solutions and fitting formulae for Lyα spectra under cylindrical geometry including recoil and velocity gradients, validated against Monte Carlo simulations.