JOURNAL ARTICLE

Noether symmetry in new extended modified f(R,G,T) theory of gravity.

  • Published In: International Journal of Geometric Methods in Modern Physics, 2024, v. 21, n. 13. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Malik, Adnan; Khan, Momina; Shamir, M. Farasat; Alkhaldi, Ali H. 3 of 3

Abstract

This study is dedicated to investigate the Noether symmetry approach in a newly introduced gravitational theory known as f (R , G , T) gravity, where f is the function of Ricci scalar R , trace of energy–momentum tensor T , and the Gauss–Bonnet invariant term G. The Noether symmetry approach plays a supportive role in generating models and then determining the exact solutions by using the conserved quantities. For this purpose, the flat Friedmann–Robertson–Walker space–time is selected to observe cosmic evolution. The set of partial differential equations is calculated in the background f (R , G , T) modified theory gravity. We further examine the four different f (R , G , T) gravity models and calculate the conserved quantities to investigate the exact solutions. The three models show the increasing behavior of the cosmic scale factor that supports the expansion of universe. In fourth model, we are able to get the corresponding conserved quantity only and it is anticipated that this model will also support the expansion of universe. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Geometric Methods in Modern Physics. 2024/11, Vol. 21, Issue 13, p1
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2024
  • ISSN:0219-8878
  • DOI:10.1142/S0219887824502177
  • Accession Number:180221392
  • Copyright Statement:Copyright of International Journal of Geometric Methods in Modern Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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