From Hubble to snap parameters: a Gaussian process reconstruction

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: H-0 = 67.2 +/- 6.2 km s(-1) Mpc(-1), q(0 )= -0.54(-0.05)(+0.06), j(0 )= 0.94(-0.18)(+0.20), and s(0 )= -0.62(-0.25)(+0.26) at 1 sigma c.l. We find that these reconstructions are compatible with the predictions from flat lambda-cold dark matter model, at least for 2 sigma confidence intervals.