FREmu: Power Spectrum Emulator for f(R) Gravity

Abstract

Abstract To investigate gravity in the nonlinear regime of cosmic structure using measurements from stage IV surveys, it is imperative to accurately compute large-scale structure observables, such as nonlinear matter power spectra, for gravity models that extend beyond general relativity. However, the theoretical predictions of nonlinear observables are typically derived from N-body simulations, which demand substantial computational resources. In this study, we introduce a novel public emulator, termed FREmu, designed to provide rapid and precise forecasts of nonlinear power spectra specifically for the Hu–Sawicki f(R) gravity model across scales 0.0089h Mpc−1 < k < 0.5h Mpc−1 and redshifts 0 < z < 3. FREmu leverages principal component analysis and artificial neural networks to establish a mapping from parameters to power spectra, utilizing training data derived from the Quijote-MG simulation suite. With a parameter space encompassing seven dimensions, including Ω m , Ω b , h, n s , σ 8, M ν , and f R 0 , the emulator achieves an accuracy exceeding 95% for the majority of cases, thus proving to be highly efficient for constraining parameters.