From <i>Hubble</i> to snap parameters: a Gaussian process reconstruction

Abstract

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, $q_0 = -0.54^{+0.06}_{-0.05}$, $j_0=0.94^{+0.20}_{-0.18}$, and $s_0=-0.62^{+0.26}_{-0.25}$ 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.