Gaussian Process Cosmography

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.

9 pages, 5 figures; v2 minor changes to match PRD version

References in corpus (8)

• Reconstructing Dark Energy
• Two new diagnostics of dark energy
• Nonparametric Dark Energy Reconstruction from Supernova Data
• Nonparametric Reconstruction of the Dark Energy Equation of State
• Mapping the Cosmological Expansion
• Model Independent Reconstruction of the Expansion History of the Universe and the Properties of Dark Energy
• Calibrating Dark Energy
• Constraining the dark fluid

Cited by in corpus (4)

• Using H(z) data as a probe of the concordance model
• Reconstruction of the Dark Energy equation of state
• Principal components of dark energy with SNLS supernovae: the effects of systematic errors
• Revisiting a model-independent dark energy reconstruction method