Gaussian process cosmography

By A. Shafieloo, A. Kim, Eric Linder. 2012

189 citations recorded by the source corpus.

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.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/d87210e31ee0515f10ea8648521d6243e5d24613

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

Citations in the corpus

DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data (2024, methodology): “Alternatively, a bottom-up approach involves reconstructing its redshift evolution directly from the data using non-parametric methods (see for example [19, 20, 25, 27, 28, 30– 35 ,”
Hubble Parameter and Baryon Acoustic Oscillation Measurement Constraints on the Hubble Constant, the Deviation from the Spatially Flat ΛCDM Model, the Deceleration–Acceleration Transition Redshift, and Spatial Curvature (2017, methodology): “Here, we use the GP method to determine a continuous function H(z) that best represents the discrete Hubble parameter data we have compiled in Table 1.6 The GP method was first used cosmologically by Holsclaw et al. (2010a, 2010b, 2011), Shafieloo et al. (2012), and Seikel et al. (2012a, 2012b).”