Accelerating universe in f (T) teleparallel gravity


Shukla B. K., SOFUOĞLU D., Khare S., Alfedeel A. H.

International Journal of Geometric Methods in Modern Physics, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1142/s0219887824502700
  • Dergi Adı: International Journal of Geometric Methods in Modern Physics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: deceleration parameter, f (T) teleparallel gravity, observational constraints
  • İstanbul Üniversitesi Adresli: Evet

Özet

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 - αT0[{1 + (T T0)2}-n - 1]. The expression consists of two free parameters, n and α, and the current value of the torsion scalar, T0. 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) = q0 + q1(z). Here, q0 and q1 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.