Minimal model dependent constraints on cosmological nuisance parameters and cosmic curvature from combinations of cosmological data

TL;DR

This paper employs a minimally model-dependent Gaussian process regression approach to constrain nuisance parameters and cosmic curvature using diverse cosmological data, finding results consistent with a flat universe.

Contribution

It introduces a method to constrain nuisance parameters and cosmic curvature simultaneously with minimal model dependence using Gaussian process regression.

Findings

Constraints on cosmic curvature are consistent with a flat universe.

Nuisance parameters are effectively constrained across multiple data sets.

Method provides priors for future cosmological data analysis.

Abstract

The study of cosmic expansion history and the late time cosmic acceleration from observational data depends on the nuisance parameters associated with the data. For example, the absolute peak magnitude of type Ia supernova associated with the type Ia supernova observations and the comoving sound horizon at the baryon drag epoch associated with baryon acoustic oscillation observations are two nuisance parameters. The nuisance parameters associated with the the gamma-ray bursts data are also considered. These nuisance parameters are constrained by combining the cosmological observations using the Gaussian process regression method with minimal model dependence. The bounds obtained in this method can be used as the prior for the data analysis while considering the observational data accordingly. Along with these nuisance parameters, the cosmic curvature density parameter is also constrained simultaneously. We find that the constraints on the cosmic curvature density parameter show no significant deviations from a flat Universe.