Forecasting Constraint on the $f(R)$ Theory with the CSST SN Ia and BAO Surveys
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910647908827136 |
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| author | Yan, Jun-Hui Gong, Yan Wang, Minglin Miao, Haitao Chen, Xuelei |
| author_facet | Yan, Jun-Hui Gong, Yan Wang, Minglin Miao, Haitao Chen, Xuelei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_17154 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Forecasting Constraint on the $f(R)$ Theory with the CSST SN Ia and BAO Surveys Yan, Jun-Hui Gong, Yan Wang, Minglin Miao, Haitao Chen, Xuelei Cosmology and Nongalactic Astrophysics 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. |
| title | Forecasting Constraint on the $f(R)$ Theory with the CSST SN Ia and BAO Surveys |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2407.17154 |