https://doi.org/10.1140/epjp/s13360-023-04442-y
Regular Article
Data analysis of three parameter models of deceleration parameter in FLRW universe
1
Laboratory of Physics of Matter and Radiation, Mohammed I University, BP 717, Oujda, Morocco
2
Department of Applied Mathematics, Delhi Technological University, 110042, Delhi, India
3
Pacif Institute of Cosmology and Selfology (PICS) Sagara, 768224, Sambalpur, Odisha, India
4
Department of Mathematics, Shyamlal College, University of Delhi, 110032, Delhi, India
5
Department of Mathematics, Indian Institute of Engineering Science and Technology, 711 103, Shibpur, Howrah, India
6
Department of Physics, Zhejiang Normal University, 321004, Jinhua, People’s Republic of China
7
New Uzbekistan University, Mustaqillik Ave. 54, 100007, Tashkent, Uzbekistan
Received:
26
April
2023
Accepted:
28
August
2023
Published online:
18
September
2023
Constraining the dark energy deceleration parameter is one of the fascinating topics in the recent cosmological paradigm. This work aims to reconstruct the dark energy using parametrization of the deceleration parameter in a flat FLRW universe filled with radiation, dark energy, and pressure-less dark matter. Thus, we have considered four well-motivated parameterizations of q(z), which can provide the evolution scenario from the deceleration to acceleration phase of the Universe. We have evaluated the expression of the corresponding Hubble parameter of each parametrization by imposing it into the Friedmann equation. We have constrained the model parameter through H(z), Pantheon, baryons acoustic oscillation (BOA), and Cosmic Microwave Background (CMB) dataset. Next, we have estimated the best-fit values of the model parameters by using Monte Carlo Markov Chain technique and implementing H(z) + BAO + SNIa + CMB dataset. Then, we analyzed the cosmographic parameter, such as deceleration, jerk, and snap parameters, graphically by employing the best-fit values of the model parameter. Moreover, we have analyzed statefinder and Om diagnostics parameters for each scenario to discriminate various dark energy models. Using the information criteria, the viability of the models has examined. In the end, we have analogized our outcomes with the standard 
CDM model to examine the viability of our models.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
