Evidence for a lower value for H0 from cosmic chronometers data?

Abstract

An intriguing discrepancy emerging in the concordance model of cosmology is the tension between the locally measured value of the Hubble rate, and the 'global' value inferred from the cosmic microwave background (CMB). This could be due to systematic uncertainties when measuring H 0 locally, or it could be that we live in a highly unlikely Hubble bubble, or other exotic scenarios. We point out that the global H 0 can be found by extrapolating H(z) data points at high-z down to z = 0. By doing this in a Bayesian non-parametric way we can find a model-independent value for H 0 . We apply this to 19 measurements based on differential age of passively evolving galaxies as cosmic chronometers. Using Gaussian processes, we find H 0 = 64.9 ± 4.2 km s −1 Mpc −1 (1σ), in agreement with the CMB value, but reinforcing the tension with the local value. An analysis of possible sources of systematic errors shows that the stellar population synthesis model adopted may change the results significantly, being the main concern for subsequent studies. Forecasts for future data show that distant H(z) measurements can be a robust method to determine H 0 , where a focus in precision and a careful assessment of systematic errors are required.

Ultra-compact structure in intermediate-luminosity radio quasars: building a sample of standard cosmological rulers and improving the dark energy constraints up to z ~ 3

“…This method was firstly proposed to test both cosmology (Holsclaw et al 2010a,b) and cosmography (Shafieloo et al 2013), and then extensively applied to the derivation of the Hubble constant H 0 (Busti et al 2014), the reconstructions of the equation of state of dark energy (Seikel et al 2013) and the distance-duality relation (Zhang 2014). The advantage of Gaussian processes is, that we do not need to assume any parametrized model for H(z) while reconstructing this function from the data (Holsclaw et al 2010a,b).…”

Null test of the cosmic curvature usingH(z)and supernovae data

“…Here, we use the Gaussian processes in Python (GaPP) [16]. This GP code has been used in various papers for different studies [16][17][18][19][20][21][22][23][24]. The distribution over functions provided by GP is suitable to describe the observational data.…”

Reconstructing the interaction between dark energy and dark matter using Gaussian processes

“…We use Gaussian processes in Python (GaPP) [27] to derive our GP results. This GP code has been applied in many papers [27][28][29][30][31][32][33]. The distribution over functions provided by GP is suitable to describe the observed data.…”