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Observational Constraints and Cosmographic Analysis of $f({T},{T}_{{G}})$ Gravity and Cosmology

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arxiv 2312.17277 v2 pith:F4MIIXOZ submitted 2023-12-27 gr-qc astro-ph.COhep-th

classification gr-qcastro-ph.COhep-th
keywords analysiscosmographicgravityobservationalwellbehaviorcosmologydark-energy
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abstract

We perform observational confrontation and cosmographic analysis of $f(T,T_G)$ gravity and cosmology. This higher-order torsional gravity is based on both the torsion scalar, as well as on the teleparallel equivalent of the Gauss--Bonnet combination, and gives rise to an effective dark-energy sector which depends on the extra torsion contributions. We employ observational data from the Hubble function and supernova Type Ia Pantheon datasets, applying a Markov chain Monte Carlo sampling technique, and we provide the iso-likelihood contours, as well as the best-fit values for the parameters of the power-law model, an ansatz which is expected to be a good approximation of most realistic deviations from general relativity. Additionally, we reconstruct the effective dark-energy equation-of-state parameter, which exhibits a quintessence-like behavior, while in the future the Universe enters into the phantom regime, before it tends asymptotically to the cosmological constant value. Furthermore, we perform a detailed cosmographic analysis, examining the deceleration, jerk, snap, and lerk parameters, showing that the transition to acceleration occurs in the redshift range $ 0.52 \leq z_{tr} \leq 0.89 $, as well as the preference of the scenario for quintessence-like behavior. Finally, we apply the Om

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Cited by 1 Pith paper

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

  1. Einstein-Gauss-Bonnet-Myrzakulov Gravity from $R + F(T, G)$: Numerical Insights and Torsion-Gauss-Bonnet Dynamics in Weitzenb\"ock Spacetime

    gr-qc 2025-05 reject novelty 2.0 of 10

    A review-style preprint that restates an R+F(T,G) modified gravity framework but provides no derivation, data, or reproducible numerical analysis.

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