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Jiachen Bai

Publications and source records attributed to Jiachen Bai.

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Testing $f(R)$ Gravity from Cosmic Shear Measurements

In this work, we perform a detailed analysis to constrain the Hu-Sawicki \(f(R)\) gravity model, using cosmic shear data from three prominent Stage-III weak lensing surveys: DES-Y3, KiDS-1000, and HSC-Y3. To accurately model the nonlinear matter clustering in the analysis of cosmic shear signals, we employ \texttt{FREmu}, a recently developed power spectrum emulator for the \(f(R)\) gravity trained on the Quijote-MG simulations. This emulator achieves precise predictions, limiting the errors to 5\% on scales of \(0.009h\,{\rm Mpc}^{-1} < k < 0.5h\,{\rm Mpc}^{-1}\). Our findings reveal that cosmic shear data alone impose only weak constraints on the \(f(R)\) parameter \(\log_{10}|f_{R_0}|\). To improve these constraints, we incorporate state-of-the-art external observations, including data from the cosmic microwave background and baryon acoustic oscillations. The inclusion of these external datasets significantly enhances the constraints, yielding an upper limit of \(\log_{10}|f_{R_0}| < -4.98\) at the 95\% confidence level.

astro-ph.CO

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

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 $0.0089 h \mathrm{Mpc}^{-1}<k<0.5 h \mathrm{Mpc}^{-1}$ 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 $Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, $M_ν$ 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.

astro-ph.CO