From Hubble to Snap Parameters: A Gaussian Process Reconstruction

Abstract

By using recent $H(z)$ and 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: $H_0=67.2\pm 6.2$ km/s/Mpc, $q_0=-0.54_{-0.05}^{+0.06}$, $j_0=0.94_{-0.18}^{+0.20}$, $s_0=-0.62_{-0.25}^{+0.26}$ at 1$\sigma$ c.l. We find that these reconstructions are compatible with the predictions from flat $\Lambda$CDM model, at least for 2$\sigma$ confidence intervals.

arXiv commentFinal version published

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