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Constraining Teleparallel Gravity through Gaussian Processes

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arxiv 2009.14582 v1 pith:BXBD663Z submitted 2020-09-29 gr-qc astro-ph.COhep-thphysics.data-an

Constraining Teleparallel Gravity through Gaussian Processes

classification gr-qc astro-ph.COhep-thphysics.data-an
keywords kernelsreconstructalloweddatadatasetsfunctionsgaussiangravity
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We apply Gaussian processes (GP) in order to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use available $H(z)$ observations from (i) cosmic chronometers data (CC); (ii) Supernova Type Ia (SN) data from the compressed Pantheon release together with the CANDELS and CLASH Multi-Cycle Treasury programs; and (iii) baryonic acoustic oscillation (BAO) datasets from the Sloan Digital Sky Survey. For the involved covariance functions, we consider four widely used choices, namely the square exponential, Cauchy, Mat\'{e}rn and rational quadratic kernels, which are consistent with one another within 1$\sigma$ confidence levels. Specifically, we use the GP approach to reconstruct a model-independent determination of the Hubble constant $H_0$, for each of these kernels and dataset combinations. These analyses are complemented with three recently announced literature values of $H_0$, namely (i) Riess $H_0^{\rm R} = 74.22 \pm 1.82 \,{\rm km\, s}^{-1} {\rm Mpc}^{-1}$; (ii) H0LiCOW Collaboration $H_0^{\rm HW} = 73.3^{+1.7}_{-1.8} \,{\rm km\, s}^{-1} {\rm Mpc}^{-1}$; and (iii) Carnegie-Chicago Hubble Program $H_0^{\rm TRGB} = 69.8 \pm 1.9 \,{\rm km\, s}^{-1} {\rm Mpc}^{-1}$. Additionally, we investigate the transition redshift between the decelerating and accelerating cosmological phases through the GP reconstructed deceleration parameter. Furthermore, we reconstruct the model-independent evolution of the dark energy equation of state, and finally reconstruct the allowed $f(T)$ functions. As a result, the $\Lambda$CDM model lies inside the allowed region at 1$\sigma$ in all the examined kernels and datasets, however a negative slope for $f(T)$ versus $T$ is slightly favored.

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