Forecasting Constraint on the $f(R)$ Theory with the CSST SN Ia and BAO Surveys
| Autor: | Yan, Jun-Hui, Gong, Yan, Wang, Minglin, Miao, Haitao, Chen, Xuelei |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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 $\Lambda$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 $\Lambda$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 $\Lambda$CDM model. This indicates that the CSST SN Ia and BAO surveys can effectively constrain and test the $f(R)$ theory. Comment: 15 pages, 3 figures, 2 tables. Accepted for publication in RAA |
| Databáze: | arXiv |
| Externí odkaz: |
|