From Hubble to snap parameters: a Gaussian process reconstruction

Abstract

By using recent H(z) and supernovae Type Ia (SNe Ia) data, we reconstruct the evolution of kinematic parameters H(z), q(z), jerk, and snap, using a model-independent, non-parametric method, namely, the Gaussian processes. Throughout the present analysis, we have allowed for a spatial curvature prior, based on Planck 18 constraints. In the case of SNe Ia, we modify a PYTHON package (GAPP) in order to obtain the reconstruction of the fourth derivative of a function, thereby allowing us to obtain the snap from comoving distances. Furthermore, using a method of importance sampling, we combine H(z) and SNe Ia reconstructions in order to find joint constraints for the kinematic parameters. We find for the current values of the parameters: H0 = 67.2 ± 6.2 km s-1 Mpc-1, q0 = -0.54+0.06-0.05, j0 = 0.94+0.20-0.18, and s0 = -0.62+0.26-0.25 at 1σ c.l. We find that these reconstructions are compatible with the predictions from flat lambda-cold dark matter model, at least for 2σ confidence intervals.