Gaussian Process Cosmography

TL;DR

This paper uses Gaussian processes to perform model-independent cosmography, reconstructing the universe's expansion history from supernova data without assuming specific cosmological models.

Contribution

It introduces a novel application of Gaussian processes for cosmography, enabling model-independent constraints on cosmic expansion parameters from observational data.

Findings

Successfully constrained Hubble and deceleration parameters without assuming dark energy models.

Validated the method against models with various features and coherence scales.

Demonstrated the effectiveness of Gaussian processes in cosmological data analysis.

Abstract

Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent manner using kinematic variables and a geometric probe of cosmology. Using the state of the art supernova distance data from the Union2.1 compilation, we constrain, without any assumptions about dark energy parametrization or matter density, the Hubble parameter and deceleration parameter as a function of redshift. Extraction of these relations is tested successfully against models with features on various coherence scales, subject to certain statistical cautions.