FREmu: Power Spectrum Emulator for f(R) Gravity

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 fR0 , the emulator achieves an accuracy exceeding 95% for the majority of cases, thus proving to be highly efficient for constraining parameters.