Constraining f(R) gravity model through Hubble Parametrization
In this work, we explore a modified theory of gravity by transitioning from standard General Relativity(GR) to an f(R) gravity framework wherein the Ricci scalar R is replaced by a general function f(R)=R+αR2. By adopting a specific Hubble parameterization H(z)=H021+(1+z)2(1+ζ)12, where H0 is the pr...
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Elsevier
2025-12-01
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| Series: | Physics Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666032625000535 |
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| author | Kshetrimayum Govind Singh Kangujam Priyokumar Singh Asem Jotin Meitei |
| author_facet | Kshetrimayum Govind Singh Kangujam Priyokumar Singh Asem Jotin Meitei |
| author_sort | Kshetrimayum Govind Singh |
| collection | DOAJ |
| description | In this work, we explore a modified theory of gravity by transitioning from standard General Relativity(GR) to an f(R) gravity framework wherein the Ricci scalar R is replaced by a general function f(R)=R+αR2. By adopting a specific Hubble parameterization H(z)=H021+(1+z)2(1+ζ)12, where H0 is the present value of Hubble parameter and ζ be the free model parameter. We investigate the dynamical evolution of the universe under this modified gravity scenario with quadratic equation of state(EoS), p=μρ2−ρ. The Raychaudhuri Equation is employed to analyze the focus of geodesics and provide insights into the expansion behavior of the model universe, allowing us to track deviations from the standard cosmological model. To assess the viability of our f(R) gravity model, we analyze 46 Hubble parameter observations using the Markov Chain Monte Carlo(MCMC) technique to constrain cosmological parameters. We further use the 1048 Pantheon dataset of Type Ia supernovae to enhance the statistical robustness and tighten constraints. The combined observational analysis supports the model as a viable alternative to the standard ΛCDM framework, particularly in explaining late-time cosmic acceleration. Notably the model exhibits deviations at higher redshifts that suggest new insights into cosmic evolution. The study also develops a neural network-based machine learning model to predict the Hubble parameter H(z) across various redshifts, facilitating data-driven insights into cosmic expansion. |
| format | Article |
| id | doaj-art-1c6c3ef3ae5c4d7ab6662d7efa3d6b7c |
| institution | Kabale University |
| issn | 2666-0326 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics Open |
| spelling | doaj-art-1c6c3ef3ae5c4d7ab6662d7efa3d6b7c2025-08-24T05:14:33ZengElsevierPhysics Open2666-03262025-12-012510030310.1016/j.physo.2025.100303Constraining f(R) gravity model through Hubble ParametrizationKshetrimayum Govind Singh0Kangujam Priyokumar Singh1Asem Jotin Meitei2Department of Mathematics, Manipur University, Canchipur, 795003, Manipur, IndiaDepartment of Mathematics, Manipur University, Canchipur, 795003, Manipur, IndiaDepartment of Mathematics, Manipur University, Canchipur, 795003, Manipur, India; Department of Mathematics, Pravabati College, Mayang Imphal, 795132, Manipur, India; Corresponding author at: Department of Mathematics, Manipur University, Canchipur, 795003, Manipur, India.In this work, we explore a modified theory of gravity by transitioning from standard General Relativity(GR) to an f(R) gravity framework wherein the Ricci scalar R is replaced by a general function f(R)=R+αR2. By adopting a specific Hubble parameterization H(z)=H021+(1+z)2(1+ζ)12, where H0 is the present value of Hubble parameter and ζ be the free model parameter. We investigate the dynamical evolution of the universe under this modified gravity scenario with quadratic equation of state(EoS), p=μρ2−ρ. The Raychaudhuri Equation is employed to analyze the focus of geodesics and provide insights into the expansion behavior of the model universe, allowing us to track deviations from the standard cosmological model. To assess the viability of our f(R) gravity model, we analyze 46 Hubble parameter observations using the Markov Chain Monte Carlo(MCMC) technique to constrain cosmological parameters. We further use the 1048 Pantheon dataset of Type Ia supernovae to enhance the statistical robustness and tighten constraints. The combined observational analysis supports the model as a viable alternative to the standard ΛCDM framework, particularly in explaining late-time cosmic acceleration. Notably the model exhibits deviations at higher redshifts that suggest new insights into cosmic evolution. The study also develops a neural network-based machine learning model to predict the Hubble parameter H(z) across various redshifts, facilitating data-driven insights into cosmic expansion.http://www.sciencedirect.com/science/article/pii/S2666032625000535Modified gravityHubble parametrizationRaychaudhuri equationObservational constraint |
| spellingShingle | Kshetrimayum Govind Singh Kangujam Priyokumar Singh Asem Jotin Meitei Constraining f(R) gravity model through Hubble Parametrization Physics Open Modified gravity Hubble parametrization Raychaudhuri equation Observational constraint |
| title | Constraining f(R) gravity model through Hubble Parametrization |
| title_full | Constraining f(R) gravity model through Hubble Parametrization |
| title_fullStr | Constraining f(R) gravity model through Hubble Parametrization |
| title_full_unstemmed | Constraining f(R) gravity model through Hubble Parametrization |
| title_short | Constraining f(R) gravity model through Hubble Parametrization |
| title_sort | constraining f r gravity model through hubble parametrization |
| topic | Modified gravity Hubble parametrization Raychaudhuri equation Observational constraint |
| url | http://www.sciencedirect.com/science/article/pii/S2666032625000535 |
| work_keys_str_mv | AT kshetrimayumgovindsingh constrainingfrgravitymodelthroughhubbleparametrization AT kangujampriyokumarsingh constrainingfrgravitymodelthroughhubbleparametrization AT asemjotinmeitei constrainingfrgravitymodelthroughhubbleparametrization |