WeChat Mini Program
Old Version Features

Comparative Study of Linear & Non-Linear $f(t)$ Gravity Models in Bianchi Type-Iii Space-Time

R. K. Mishra, Rahul Sharma

Astrophysics and Space Science(2025)

Sant Longowal Institute of Engineering and Technology

Cited 0|Views1
Abstract
In this study, we explore the dynamics of Bianchi type-III space-time within the framework of f(T) gravity, focusing on both linear and non-linear forms of f(T) function. We analyze the behavior of cosmological parameters by assuming the deceleration parameter (DP) as a simple linear function of the Hubble parameter. Key cosmological parameters such as the scale factor, Hubble parameter, DP, spatial volume, shear scalar, expansion scalar, energy density, pressure, and the equation of state (EoS) parameter are expressed in terms of the redshift parameter. Their dynamic behaviors are graphically presented for both linear and non-linear forms of f(T) gravity. Our results align with recent cosmological observations, with the non-linear form of f(T) exhibiting a stronger tendency toward accelerated cosmic expansion compared to the linear model. The EoS parameter indicates a quintessence phase, driving the universe’s accelerated expansion, as recently investigated by Varshney et al. (Can. J. Phys. 102(3):199–209, 2023). Additionally, we examine the violation of the strong energy conditions, a crucial aspect in modified gravity theories. The model parameter ξ and the current value of the Hubble parameter H_0 are estimated using the Hubble data set and Pantheon+ SHOES data set, further validating our theoretical model.
More
Translated text
Key words
gravity,Bianchi type-III space time,Cosmological model,Observational data
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined