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Noether Symmetry Approach in Scalar-Torsion $f(T,\phi)$ Gravity

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arxiv 2210.11944 v3 pith:LHS4XEPQ submitted 2022-10-19 gr-qc hep-th

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keywords noethersymmetryapproachcosmologicalgravityparametersolutionsdemonstrate
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abstract

The Noether Symmetry approach is applied to study an extended teleparallel $f(T,\phi)$ gravity that contains the torsion scalar $T$ and the scalar field $\phi$ in the context of an Friedmann-Lema\^{i}tre-Robertson-Walker space-time. We investigate the Noether symmetry approach in $f(T,\phi)$ gravity formalism with the specific form of $f(T,\phi)$ and analyze how to demonstrate a nontrivial Noether vector. The Noether symmetry method is a helpful resource for generating models and finding out the exact solution of the Lagrangian. In this article, we go through how the Noether symmetry approach enables us to define the form of the function $f(T,\phi)$ and obtain exact cosmological solutions. We also find the analytical cosmological solutions to the field equations, that is consistent with the Noether symmetry. Our results demonstrate that the obtained solutions enable an accelerated expansion of the Universe. We have also obtained the present value of the Hubble parameter, deceleration parameter, and effective equation of state parameter, which is fit in the range of current cosmological observations.

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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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