Updated F(T) gravity constraints from high redshift cosmography

TL;DR

This paper uses high-redshift cosmography with observational data to constrain f(T) teleparallel gravity models, providing a model-independent way to test gravity theories without solving dynamical equations.

Contribution

It introduces a method to map cosmographic parameters into constraints on f(T) gravity, enabling model testing directly from observational data.

Findings

Constraints on f(T) and its derivatives from cosmography.

Model-independent tests of teleparallel gravity.

Use of MCMC for parameter estimation.

Abstract

In the last dozen years a wide and variegated mass of observational data revealed that the universe is now expanding at an accelerated rate. In the absence of a well-based theory to interpret the observations, cosmography provides information about the evolution of the Universe from measured distances, only assuming that the geometry of the can be described by the Friedmann-Lemaitre-Robertson -Walker metric. We perform a high-redshift analysis allows us to put constraints on the cosmographic parameters up to the 5fth order, thus inducing indirect constraints on any gravity theory. Here we are interested in the so called teleparallel gravity theory, f(T). Actually we use the analytical expressions of the present day values of f(T) and its derivatives as functions of the cosmographic parameters to map the cosmography region of confidences into confidence ranges for f(T) and its derivative. Moreover, we show how these can be used to test some teleparallel gravity models without solving the dynamical equations. Our analysis is based on the Union2 Type Ia Supernovae (SNIa) data set, a set of 28 measurements of the Hubble parameter, the Hubble diagram constructed from some Gamma Ray Bursts (GRB) luminosity distance indicators, and gaussian priors on the distance from the Baryon Acoustic Oscillations (BAO), and the Hubble constant h. To perform our statistical analysis and to explore the probability distributions of the cosmographic parameters we use the Markov Chain Monte Carlo Method (MCMC).