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arXiv · 2407.17154

Forecasting Constraint on the $f(R)$ Theory with the CSST SN Ia and BAO Surveys

Jun-Hui Yan·Yan Gong·Minglin Wang·Haitao Miao·Xuelei Chen
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Abstract

The $f(R)$ modified gravity theory can explain the accelerating expansion of the late Universe without introducing dark energy. In this study, we predict the constraint strength on the $f(R)$ theory using the mock data generated from the China Space Station Telescope (CSST) Ultra-Deep Field (UDF) Type Ia supernova (SN Ia) survey and wide-field slitless spectroscopic baryon acoustic oscillation (BAO) survey. We explore three popular $f(R)$ models, and introduce a parameter $b$ to characterize the deviation of the f(R) theory from the $Λ$CDM theory. The Markov Chain Monte Carlo (MCMC) method is employed to constrain the parameters in the $f(R)$ models, and the nuisance parameters and systematical uncertainties are also considered in the model fitting process. Besides, we also perform model comparisons between the $f(R)$ models and the $Λ$CDM model. We find that the constraint accuracy using the CSST SN Ia+BAO dataset alone is comparable to or even better than the result given by the combination of the current relevant observations, and the CSST SN Ia+BAO survey can distinguish the $f(R)$ models from the $Λ$CDM model. This indicates that the CSST SN Ia and BAO surveys can effectively constrain and test the $f(R)$ theory.

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Jun-Hui Yan, Yan Gong, Minglin Wang, Haitao Miao, Xuelei Chen. 2024-10-14. Forecasting Constraint on the $f(R)$ Theory with the CSST SN Ia and BAO Surveys. https://arxiv.org/abs/2407.17154

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