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Dynamical systems analysis in $f(T,\phi)$ gravity

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arxiv 2209.03414 v2 pith:5JQD5Z5S submitted 2022-09-03 gr-qc hep-th

classification gr-qchep-th
keywords gravitymodelsscalartorsionextensionsgeneralanalysisbeen
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abstract

Teleparallel based cosmological models provide a description of gravity in which torsion is the mediator of gravitation. Several extensions have been made within the so-called Teleparallel equivalent of general relativity which is equivalent to general relativity at the level of the equations of motion where attempts are made to study the extensions of this form of gravity and to describe more general functions of the torsion scalar $T$. One of these extensions is $f(T,\phi)$ gravity; $T$ and $\phi$ respectively denote the torsion scalar and scalar field. In this work, the dynamical system analysis has been performed for this class of theories to obtain the cosmological behaviour of a number of models. Two models are presented here with some functional form of the torsion scalar and the critical points are obtained. For each critical point, the stability behaviour and the corresponding cosmology are shown. Through the graphical representation the equation of state parameter and the density parameters for matter-dominated, radiation-dominated and dark energy phase are also presented for both the models.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Higgs-like inflation in scalar-torsion $f(T,\phi)$ gravity in light of ACT-SPT-DESI constraints

    gr-qc 2025-12 conditional novelty 5.0 of 10

    Higgs-like inflation in f(T,φ) torsion gravity can accommodate the ACT/DESI upward shift in the scalar spectral index while predicting a tensor-to-scalar ratio r≈0.01–0.04.

  2. Late Time Phenomena in $f(T,\mathcal{T})$ Gravity Framework: Role of $H_0$ Priors

    gr-qc 2024-11 conditional novelty 4.0 of 10

    An f(T,T) gravity model fitted to Pantheon+, BAO, and cosmic chronometer data yields a range of H0 posteriors that track the input priors, and predicts a growth rate about 9-11% below ΛCDM for two data combinations.

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