Reconstructing teleparallel gravity with cosmic structure growth and expansion rate data

Abstract

We consider the application of a machine learning technique to growth and expansion rate data in the context of teleparallel gravity (TG). We do this by using a combined approach of Hubble data together with redshift-space-distortion f σ 8 data which together are used to reconstruct the TG Lagrangian via Gaussian processes (GP), where the Hubble data mainly comes from cosmic chronometer and supernova type Ia data from the Pantheon release. In this work, we consider two main GP covariance functions, namely the squaredexponential and Cauchy kernels in order to show consistency (to within 1σ uncertainties). The core results of this work are the numerical constructions of the TG Lagrangian from GP reconstructed Hubble and growth data. We take different possible combinations of the datasets and kernels to show any potential differences in this regard. We show that nontrivial cosmology beyond ΛCDM falls within the uncertainties of the reconstruction from growth data.

Teleparallel Gravity: From Theory to Cosmology

“…10.11 and detailed in Ref. [695] the role of the growth data was studied by reconstructing observations from RSD f σ 8 data together with the Hubble data that comes from cosmic chronometer and SN type Ia data. The advantage of this reconstruction, is that it acquires more simulated data points in order to constrain modified gravity models with better precision.…”

“…Thus, given a set of Gaussian distributed data points, GP provide an iterative process by which to produce the most likely underlying continuous function that describes the data together with its associated confidence bounds without assuming a prescribed form of the function [951]. In the current era of precision cosmology with multiple state-of-the-art surveys, GP has been exhaustively applied in cosmology [960,951,1092,233,1048,1105,240,773,528,1095,43,700,706,1093,428,695], particularly to infer the late-time dynamics of the Universe.…”

Gaussian processes and effective field theory of $f(T)$ gravity under the $H_0$ tension

“…Hence, Gaussian processes form a model-independent function reconstruction method without any special physical assumption and parameterization. Therefore, they are widely used in cosmological researches to reconstruct physical parameters from observational data sets [66][67][68][69][70][71][72][73][74][75][76].…”

“…The concept of EFT has been widely applied to cosmological studies [58][59][60][61][62][63], and this approach was developed recently for torsional gravity [64,65]. On the other hand, the Gaussian processes regression provides us a reliable way to obtain fitting functions directly from observational data, and it has been widely used to reconstruct non-linear functions [66][67][68][69][70][71][72][73][74][75][76][77][78][79]. With this approach, we are able to analyse Hubble parameter observational data without any special assumption or specific model.…”

Neural network reconstruction of H'(z) and its application in teleparallel gravity

“…Other measurements exist that hold the promise of offering new calculations of H 0 that do not rely on electromagnetic observations such as the novel approach of gravitational wave standard sirens [26] but the precision of such methods is not competitive with standard approaches as of yet. In this context, we endeavour to extend the literature in the direction of producing nonparametric estimates of the Hubble parameter together with its derivative which are core to a number of important themes of research such as cosmography [27][28][29] and modified gravity [30][31][32][33][34].…”