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Constraints on f(Q) logarithmic model using gravitational wave standard sirens
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Constraints on $f(Q)$ logarithmic model using gravitational wave standard sirens
abstract
In this paper, we study the constraints on the $f(Q)=Q/(8\pi G) - \alpha \ln(Q/Q_0)$, symmetric teleparallel model using local measurements and gravitational wave mock standard sirens. Using observational local SNIa and BAO data and energy conditions, the logarithmic $f(Q)$ model is capable of explaining the cosmic late-time acceleration by geometrical means. This result suggests that the logarithmic symmetric teleparallel model could be a candidate to solve the cosmological constant problem. In the case of the simulated standard siren data, by using the performance of the future ET and LISA detectors, we expect to be able to measure the current Hubble constant $H_0$, and the matter content $\Omega_m$, with a precision better than 1\% and 6\%, respectively. Furthermore, we explore the predicted $f(Q)$ logarithmic model deviation from the standard GR using ET and LISA mock standard sirens. The ratio $d_L^{\text{gw}}(z)/d_L^{\text{em}}(z)$, which quantifies the deviation from GR gives us a significant deviation higher than 13\% at $z=1$, and it continues growing to reach a deviation higher than 18\% in its median value. Future standard siren data will be able to quantify the strength of the deviation from GR and hence whether a cosmology like the one implied by this $f(Q)$ model is feasible.
Forward citations
Cited by 3 Pith papers
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Constraints on Logarithmic Model Extensions of Symmetric Teleparallel Gravity
Two new logarithmic f(Q) gravity models fit current cosmological data and predict contrasting, testable deviations in the effective gravitational coupling and gravitational-wave damping.
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Dynamical Dark Energy or Modified Gravity? Signatures in Gravitational Wave Propagation
Reconstructing the dark energy density from DESI BAO and DESyr5 supernovae, then recasting it as f(Q) gravity, predicts a low-redshift gravitational wave damping ν≈0.18 (≳2σ from GR) only for the DESyr5 dataset.
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