FREmu: Power Spectrum Emulator for $f(R)$ Gravity

Abstract

To investigate gravity in the non-linear regime of cosmic structure using measurements from Stage-IV surveys, it is imperative to accurately compute large-scale structure observables, such as non-linear matter power spectra, for gravity models that extend beyond general relativity. However, the theoretical predictions of non-linear 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 non-linear power spectra specifically for the Hu-Sawicki $f(R)$ gravity model across scales $0.0089h{\mathrm{Mpc}}^{-1}<k<0.5h{\mathrm{Mpc}}^{-1}$ 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 7 dimensions, including ${\Omega }_m$, ${\Omega }_b$, $h$, $n_s$, ${\sigma }_8$, $M_{\nu }$ 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.

12 pages, 5 figures, 1 table, accepted by The Astrophysical Journal (ApJ)

References in corpus (6)

• Models of f(R) Cosmic Acceleration that Evade Solar-System Tests
• HMcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback
• Cosmic Calibration: Constraints from the Matter Power Spectrum and the Cosmic Microwave Background
• FORGE -- the f(R) gravity cosmic emulator project I: Introduction and matter power spectrum emulator
• The e-MANTIS emulator: fast predictions of the non-linear matter power spectrum in $f(R)$CDM cosmology
• Constraints on $f(R)$ and nDGP Modified Gravity Model Parameters with Cluster Abundances and Galaxy Clustering

Cited by in corpus (7)

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• Towards unveiling the large-scale nature of gravity with the wavelet scattering transform
• Cosmological Inference with Cosmic Voids and Neural Network Emulators
• Principal Components for Model-Agnostic Modified Gravity with 3x2pt
• Power Spectrum Emulators from Neural Networks and Tree-Based Methods
• Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity
• Addressing Dipole Tension via Clustering in $\Lambda$CDM and beyond