A data-driven Reconstruction of Horndeski gravity via the Gaussian processes

TL;DR

This paper uses Gaussian processes to reconstruct the universe's expansion history from observational data and derives constraints on Horndeski gravity theories, including quintessence and designer models, based on this reconstruction.

Contribution

It introduces a data-driven approach to reconstruct Horndeski gravity models directly from expansion history data using Gaussian processes.

Findings

Reconstructed the Hubble function from multiple observational datasets.

Derived constraints on Horndeski potentials and dark energy equation of state.

Compared different Horndeski formalisms using the reconstructed expansion history.

Abstract

We reconstruct the Hubble function from cosmic chronometers, supernovae, and baryon acoustic oscillations compiled data sets via the Gaussian process (GP) method and use it to draw out Horndeski theories that are fully anchored on expansion history data. In particular, we consider three well-established formalisms of Horndeski gravity which single out a potential through the expansion data, namely: quintessence potential, designer Horndeski, and tailoring Horndeski. We discuss each method in detail and complement it with the GP reconstructed Hubble function to obtain predictive constraints on the potentials and the dark energy equation of state.