Accelerating universe in f(T) teleparallel gravity

被引:0
|
作者
Shukla, Bhupendra Kumar [1 ]
Sofuoglu, Deger [2 ]
Khare, Sandeep [3 ]
Alfedeel, Alnadhief H. A. [4 ,5 ,6 ]
机构
[1] Govt Coll, Dept Math, Bandri Sagar 470442, MP, India
[2] Istanbul Univ, Dept Phys, TR-34134 Istanbul, Turkiye
[3] Indra Gandhi Natl Tribal Univ, Dept Math, Amarkantak, MP, India
[4] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Dept Math & Stat, Riyadh 13318, Saudi Arabia
[5] Univ Khartoum, Fac Sci, Dept Phys, POB 321, Khartoum 11115, Sudan
[6] North West Univ, Ctr Space Res, ZA-2520 Potchefstroom, South Africa
关键词
f(T) teleparallel gravity; deceleration parameter; observational constraints;
D O I
10.1142/S0219887824502700
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In recent decades, there has been significant research on the role of torsion in gravity, with a focus on aligning gravity with its gauge formulation and including spin into a geometric description. In order to account for the present phenomenon of the universe's accelerated expansion, recent developments have introduced f(T) theories that rely on the disparities found in teleparallel gravity. Torsion, rather than curvature, is the fundamental geometric property that describes gravity in these theories. When compared to theories involving f(R) functions, the field equations are consistently of second order and surprisingly simple. We consider a specific type of function called torsion, which is defined as f(T) = T - alpha T-0[{1 + ( T/T-0 )(2)}(-n) - 1]. The expression consists of two free parameters, n and alpha, and the current value of the torsion scalar, T-0. In order to solve the modified torsion field equations (MTFEs), we can utilize the parametrization of the deceleration parameter (DP) in terms of redshift, denoted as q(z) = q(0) + q(1)(z). Here, q(0) and q(1) represent the model parameters. The model parameters are determined by utilizing observable constraints, including as 57 Hubble data points, 1048 Pantheon supernovae type Ia data, and Baryon Acoustic Oscillations (BAO) datasets. In addition, we utilize Markov Chain Monte Carlo (MCMC) methods for statistical analysis.
引用
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页数:19
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